Mapping Technology Space by Normalizing Patent Networks
Jeff Alstott*
Giorgio Triulzi*
Bowen Yan
Jianxi Luo
Technology is a complex system, with technologies relating to each other in a space that can be mapped as a network. The technology network's structure can reveal properties of technologies and of human behavior, if it can be mapped accurately. Technology networks have been made from patent data, using several measures of proximity. These measures, however, are influenced by factors of the patenting system that do not reflect technologies or their proximity. We introduce a method to precisely normalize out multiple impinging factors in patent data and extract the true signal of technological proximity, by comparing the empirical proximity measures with what they would be in random situations that remove the impinging factors. With this method, we created technology networks, using data from 3.9 million patents. After normalization, different measures of proximity became more correlated with each other, approaching a single dimension of technological proximity. The normalized technology networks were sparse, with few pairs of technology domains being significantly related. The normalized network corresponded with human behavior: we analyzed the patenting histories of 2.8 million inventors and found they were more likely to invent in two different technology domains if the pair was closely related in the technology network. We also analyzed 250 thousand firms' patents and found that, in contrast, firms' inventive activities were only modestly associated with the technology network; firms' portfolios combined pairs of technology domains about twice as often as inventors. These results suggest that controlling for impinging factors provides meaningful measures of technological proximity for patent-based mapping of the technology space, and that this map can be used to aid in technology innovation planning and management. \keywords{technology \and networks \and patents \and invention \and technology diversification
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\author{Jeff Alstott*}
\email{alstott@mit.edu}
\affiliation{Massachusetts Institute of Technology}
\affiliation{Singapore University of Technology and Design}
\author{Giorgio Triulzi*}
\email{gtriulzi@mit.edu}
\affiliation{Massachusetts Institute of Technology}
\affiliation{Singapore University of Technology and Design}
\affiliation{United Nations University - MERIT}
\author{Bowen Yan}
\affiliation{Singapore University of Technology and Design}
\author{Jianxi Luo}
\affiliation{Singapore University of Technology and Design}

\title{Mapping Technology Space by
Normalizing Patent Networks}

\begin{abstract}
Technology is a complex system, with technologies relating to each other in a space that can be mapped as a network. The technology network's structure can reveal properties of technologies and of human behavior, if it can be mapped accurately. Technology networks have been made from patent data, using several measures of proximity. These measures, however, are influenced by factors of the patenting system that do not reflect technologies or their proximity. We introduce a method to precisely normalize out multiple impinging factors in patent data and extract the true signal of technological proximity, by comparing the empirical proximity measures with what they would be in random situations that remove the impinging factors. With this method, we created technology networks, using data from 3.9 million patents. After normalization, different measures of proximity became more correlated with each other, approaching a single dimension of technological proximity. The normalized technology networks were sparse, with few pairs of technology domains being significantly related. The normalized network corresponded with human behavior: we analyzed the patenting histories of 2.8 million inventors and found they were more likely to invent in two different technology domains if the pair was closely related in the technology network. We also analyzed 250 thousand firms' patents and found that, in contrast, firms' inventive activities were only modestly associated with the technology network; firms' portfolios combined pairs of technology domains about twice as often as inventors. These results suggest that controlling for impinging factors provides meaningful measures of technological proximity for patent-based mapping of the technology space, and that this map can be used to aid in technology innovation planning and management.
\keywords{technology \and networks \and patents \and invention \and technology diversification}

\end{abstract}


\maketitle

\section{Introduction}
Technological invention can be considered as navigating a space of technologies \cite{Kauffman2000, Strumsky2002, Fleming2004, Silverberg2005, Frenken2006, Silverberg2007}. Networks have been used to represent and describe that space: there are many kinds of technologies, and they relate to each other in many, complex ways \cite{Kay2014, Leydesdorff2014, Breschi2003}. Different technological domains can be connected and proximate in the technology space if they rely on similar or related knowledge \cite{Leydesdorff2014, Arthur2009, Shiffrin2004,Mane2004, Verspagen1997a, Jaffe1986}. An accurate network map of the technology space, even at a low resolution, opens the door to understanding how technology as a whole behaves and how humans interact with it. Such understanding could improve the inventive strategies of individual inventors and the technology innovation policies of firms or countries.

The technology space can be mapped using patent data, and technology domains identified from patent metadata. Domain experts at patent offices classify every patent into one of many technology classes, such as ``organic chemistry" or ``hats," which represent technology domains. The proximity between two technology classes can be measured in numerous ways using patent data, and different measures reflect different intuitions of how technologies could be related or similar with each other. This creates two types of challenges when mapping the space of technologies. First, when different measures disagree on the proximity of two technology domains, it can make interpretation of the technology space difficult. This makes it harder to derive precise technology development strategies based on the network map of the technology space. If different methods for quantifying proximity could be harmonized, it would allow for greater clarity in studying technology. Second, the different empirical measures of inter-domain distance in the technology space are affected by biases that arise from the patenting and inventive processes, which distort the perceived proximity across technologies. Hence, these measures needs to be properly cleaned to allow the true representation of the technology space to emerge.

\begin{figure*}[ht!]
\begin{center}
\includegraphics[width=\textwidth]{figs/Networks.pdf} 
\end{center}
\caption{\textbf{Measuring technology networks that control for impinging factors reveals different network structures.} A) The empirical and B) normalized technology networks. The normalized network's links are not influenced by the impinging factors of Fig. \ref{Impinging_Factors}. Node size: proportional to the number of patents in the technology class. Link weight: proportional to the number of citations between the technology classes (the average of both directions). The networks are fully connected, but only a subset of the strongest links are visualized (see \textit{Appendix} \ref{network_visualization}). For visual reference, a community structure is shown by node color, which was identified using both visualized and unvisualized links.
}\label{Empirical_Normalized_Networks}
\end{figure*}

We used data from 3,911,050 utility patents issued from 1976 to 2010 by the United States Patent and Trademark Office to create technology networks using nine different measures of proximity. Fig. \ref{Empirical_Normalized_Networks}A shows one such network. Each of the proximity measures builds on one of two phenomena: 1) patents' citations to other patents in different technology classes, or 2) classes occurring together on patents or in the patenting histories of inventors or firms. Both of these phenomena, are influenced by impinging factors that are not intrinsic properties of the technologies that the patents represent (Fig. \ref{Impinging_Factors}). These factors include classes' number of patents, the number of citations those patents make, and how old the patents are; we examine their origins below. These factors distort the perception of how distant two technologies are in the space based on raw empirical measurements. The accuracy of a technology network map crucially depends on the ability to separate the true signal of technological proximity from these other, spurious effects. By removing the spurious effects, we can create a better representation of the latent technology network (Fig. \ref{Empirical_Normalized_Networks}B).


Here we introduce a method to precisely control for the complexities of multiple impinging factors in patent data, all at once. We calculated a null hypothesis: an expectation of what the observed proximity measure between two technology classes would be by random chance, given the other influencing factors. We then identified which pairs of technology classes had significantly higher proximity than that expected by chance. After normalizing the empirical proximity measures relative to the random expectation, most measures of technology proximity became highly correlated with each other. Normalized proximity measures based on citations, co-classification, and inventors' patent portfolios are all correlated strongly. The sole measure of technology class proximity with lower correlation to the others was how often firms patented in a pair of classes, which could be driven by factors beyond how proximate the technologies are. These results indicate that controlling for the impinging factors creates stronger agreement of different measures of proximity, validating the utility of the method. The increased agreement of measures also opens up the possibility of measuring a single technology space and constructing a unified patent technology map.

\section{Measuring Technology Proximity}\label{measuresreview}
The proximity of technologies has been measured in various ways. Here we briefly review the most common proximity measures. The proximity measures can be organized into two families based on the kind of data they use: citations and co-occurrences.

\subsection{Citation based measures}
Patents cite other patents as related technologies. Citations highlight which existing solutions the current invention has improved upon, with the purpose of limiting what the citing patent can claim as novel intellectual property. Citations can thus represent knowledge proximity. Several measures of knowledge proximity between technology classes have been created in the literture, building upon patent citations.

\subsubsection{Direct Citation}
The most straightforward way to describe the proximity between two technology classes is to simply count the number of citations between them \cite{Leten2007}. The Direct Citation measure is the total number of citations from patents in a class $X$ to other patents in another class $Y$. Because citations disclose the relevant prior art, the direct citation count between classes can be interpreted as an overall measure of the importance of the cited class as a technical input for the citing one. 

\subsubsection{Co-Citation}
Patents can make many citations, including to patents from multiple classes. If two classes are often cited together they may function well together as a input. The Co-Citation between two classes $X$ and $Y$ is the number of patents that cited patents from both $X$ and $Y$. Co-Citation thus indicates if two classes often jointly serve as knowledge inputs for the same inventive output. Co-Citation has been used to measure the proximity of scientific fields and journals \cite{Uzzi2013, Wallace2009}. Co-Citation is sometimes normalized by computing the Jaccard index (dividing the number of co-citations by the total number of citations received by patents in the two classes $X$ and $Y$ \cite{SmallHenry1973}), but here we calculate Co-Citation directly and use more complex normalizations, as described below.

\subsubsection{Cosine Similarity}
A more sophisticated measure of proximity is not whether two classes cite each other, but if they cite other classes in a similar pattern (i.e. if they use the same set of inventive inputs). This is analogous to measuring the structural equivalence of two nodes in a network \cite{Leicht2006}. We count how many citations patents in a class $X$ make to patents in every other class ($Y$, $Z$, and so on). This is summarized as a class-class citation vector, $c_X$. If the class-class citation vector is the same for two classes, they have the same citation behavior, and are taken to be related or proximate. If they have entirely different vectors, they have entirely different citation behaviors, and are taken to be unrelated. We calculate the similarity of the two class-class citation vectors by taking the cosine of the angle between them, $cos(c_X, c_Y)$. 

Cosine Similarity is a long-used measure for evaluating the similarity of two sets. The cosine index was introduced as a measure of proximity of technology domains in patent data by Jaffe \cite{Jaffe1986, Jaffe1989}. Jaffe measured relatedness between pairs of technological fields (proxied by patent classes) by computing the cosine of the vectors representing the occurrences of fields in firms' patent documents. Breschi and colleagues \cite{Breschi2003} designed a similar version of the index, which measures proximity between class pairs as the cosine of the classes' vectors of co-occurrences in patent documents. Cosine similarity has been used in other studies to create patent-based technology maps \cite{Kay2014, Leydesdorff2014}.

Cosine Similarity can be calculated using two different class-class citation vectors: the vector of citations the class $X$ \textit{makes} to every other class, and the vector of citations the class \textit{receives} from every other class. These can be thought of as measuring the similarity of knowledge inputs to the class vs. the similarity of knowledge outputs of the class. We refer to these two measures as Cosine Similarity, Inputs and Cosine Similarity, Outputs.

The principle of measuring class-class citation vectors can be extended to class-patent citation vectors \cite{Yan2015}. In this case, what is measured is how many citations patents in a class $X$ make to every individual patent, without summarizing them by which classes those patents are in. This creates a much higher dimensional vector (as there are many more patents than classes), but the principle is the same. Obviously, the citation vector measured at the class-patent citation level has a higher granularity than its class-class counterpart. This can be interpreted as a measure of similarity of specific, rather than generic, knowledge inputs or outputs between classes. The class-patent citation vectors between two classes can again be compared using Cosine Similarity. Again there are two versions of the measure, depending on whether we measure the citations a class makes versus the citations a class receives. We refer to these two measures as Cosine Similarity, Input , High Resolution and Cosine Similarity, Output, High Resolution. 

\subsection{Co-Classification and Co-Occurrence based measures}
Patents are assigned a main class to which they primarily belong, which is the class used for the citation analysis. But patents are also frequently assigned to additional, secondary classes. We can then measure how often two classes both appear on the same patents together (Co-Classification). This is a common method in scientometric analysis \cite{Jeong2015, Dolfsma2011, Joo2010, Joo2009, Leydesdorff2008, Engelsman1994}. Similarly, an inventor or firm can have multiple patents, and those patents could be in multiple classes. We can then measure how often two classes both appear together in inventors' and firms' patent histories (Co-Occurrence).  Co-Classification is interpreted as measuring how often two technology domains are combined into an invention, while Co-Occurrence is interpreted as how often two technology domains are both used within a single mind or collection of minds (i.e. a firm). Therefore, Co-Classification is a measure of proximity of two technologies based on how similar their artifacts are. In contrast, Co-Occurrence measures the similarity in the technical skills required to make the artifacts, or in the assets or managerial practices needed to be successful in both \cite{Bryce2009, Bottazzi2010, Teece1994}.

Calculating Co-Occurrence from patent data requires accurately tracking individual inventors and firms with multiple patents, even though their names can be listed differently on different patents (e.g. ``IBM'' vs. ``International Business Machines'' or ``Charles Jacob Smith'' vs ``Charles J. Smith''). The recent availability of harmonized inventors' and firms' names for patent data \cite{Li2014} made it possible to compute a reliable measure of Co-Occurrence of technology classes in inventors' and firms' patenting histories. Following the work done in \cite{Yan2015}, this paper represents one of the first attempts at measuring the Co-Occurrence of technology classes in inventors' patenting histories. Inventor and firm identities were tracked across patents using name reconciliation data from \cite{Li2014}. This data identified 2,756,508 inventors and 247,913 firms. Firm identity reconciliation, performed by \cite{Li2014} and based on \cite{Hall2001}, focused on linking patents' assignee names to firms traded in the United States stock market and harmonizing spell variations. The firm identity reconciliation did not merge firms' subsidiaries, which can be distinct entities with different knowledge, capabilities, and operations. Interpreting subsidiaries' relationships with each other is a complex topic that we will not seek to resolve here; we simply consider subsidiaries as separate entities.

\section{Origins of Factors Impinging on the Empirical Measurement of Technology Proximity}

Unfortunately, all the measures of technology proximity are affected by factors other than the technologies themselves. These factors thus impinge on the measures of proximity, detracting from the signal we desire to measure. The impinging factors that we can control for are different depending on if the proximity measure is based on citations or on occurrence data.

\subsection{Citations}
The probability of a citation between two patents, or between two technology classes, is affected by several variables that are not intrinsic properties of the inventions they represent \cite{Hall2001, Uzzi2013}. The expected number of citations between any two technology classes depends on several factors, which vary greatly across classes and time (Fig. \ref{Impinging_Factors}).

\begin{figure}[ht!]
\begin{center}
\includegraphics[width=\columnwidth]{figs/Impinging_Factors_IPC.png} 
\end{center}
\caption{\textbf{Multiple aspects of the patenting system affect citation rates, and thus impinge on measures of proximity.} The impinging factors vary greatly between technology classes and are not stable over time, but these complexities can be removed from measures of technology proximity by normalizing with randomized controls. Colored lines: Individual classes. Black lines: Averages.
A) The number of patents in different technology classes, over time.
B) The number of citations between every pair of classes compared to the number of patents in the two classes (on a double logarithmic scale). On average the two values are proportional.
C) The mean number of citations made by patents in different technology classes, over time. 
D) The percent of citations that cite patents in classes different from that of the citing patent, grouped by the class of the citing patent, over time.
E) The distribution of the ages of patents that are cited, grouped by the year the citation was made.
F) The average age of patents in different technology classes over time. The data for E and F are censored, as they only cover patents awarded from 1976 onward. 
}\label{Impinging_Factors}
\end{figure}

First, the expected number of citations between a citing class and a cited class is driven by the number of patents in each (Fig. \ref{Impinging_Factors}A,B). Technology classes vary greatly in size, and those sizes change over time (Fig. \ref{Impinging_Factors}A). All else equal, larger classes both make and receive more citations, and thus there is a linear correlation between the sizes of two technology classes and the number of citations between them (Fig. \ref{Impinging_Factors}B).

Another possible influence of examiner behavior changing over time is the number of citations made per patent. The number of citations made per patent varies across technology classes and history, and is increasing over time (Fig. \ref{Impinging_Factors}C). This may be a cognitive bias due to the growing pool of potential prior art (more previous patents) and patent electronic databases, which makes searching for prior art easier and thus assessing novelty more stringent.  

Patents' citations are also biased to be made to patents within the same class. Citations are often made by patent examiners \cite{Criscuolo2008, Alcacer2006}, and the examiners leverage the classification system to make citations. They first classify the patent, then search for potentially related patents \cite{2014}, and so are more likely to find relevant previous patents to cite within the same class. The portion of citations made to patents in other classes varies greatly depending on the class of the citing patent, and is also growing over time (Fig. \ref{Impinging_Factors}D); these differences can be due to differences in examiner behavior or office policy. 

Another factor affecting citations is time. Recent inventions need time to be recognized and older technologies gradually become unused, though can potentially remain indefinitely \cite{Hall2001,Valverde2007}. Since a patent's citations reflect what technologies are relevant prior art to the invention, these temporal effects are reflected in the citation record: Fig. \ref{Impinging_Factors}E shows the distribution of the age of patents at the time they are cited. Obviously, because the patent data we studied only extends to 1976, the maximum possible patent age depends on the year in which the citing patents was awarded.
The increased likelihood of citing patents from a particular point in history interacts with the growing number of new patents and the increasing number of citations they make. It also crucially interacts with the fact that technology classes vary in age. The average age of patents in a technology class varies across classes, and is increasing over history (Fig. \ref{Impinging_Factors}F). The trend is increasing partly by construction, as we have no data before 1976, but the large variance for recent years is likely a real phenomenon.

Given all these citation phenomena, the expected number of citations between any two technology classes are influenced by their propensity to cite and be cited by other classes, their number of patents, the age distribution of their patents and their propensity to make and receive citations. In this study we show how to simultaneously control for all these factors.

\subsection{Co-Classification and Co-Occurrence}
Like citations, Co-Classification and Co-Occurrence measures are also influenced by other impinging factors, such as the simple number of occurrences. The probability that two technology classes co-occur within the same patent document, inventor's or firm's past patenting history depends on the number of classes that are associated with a patent, inventor or firm and the number of patents, inventors and firms that are associated with a given technology class. A given technology class may be very common or very rare across all patents, inventors, or firms. Similarly, each patent, inventor, or firm may associate with very many technology classes, or very few. 

As explained by Bottazzi and Pirino \cite{Bottazzi2010}, in order to properly measure the true proximity between classes as a function of their Co-Occurrence it is crucial to compare the observed Co-Occurrence with a null hypothesis in which occurrences of classes in patents, inventors' and firms' histories are randomly distributed while preserving \textit{both} the number of occurrences of a class \textit{and} the number of classes that are associated with a given patent, inventor or firm. This is necessary to make sure that the random expectations incorporate the characteristic skewed distributions of the number of classes per patent/inventor/firm and the number of patents/inventors/firms per class that are observed in the real world.

Controlling for the number of occurrences in co-occurrence data has been addressed in information science, ecology, medicine and economics \cite{Eck2009,Ulrich2007,Stone1990,Gobbi2014,Neffke2008}.
We extend on this understanding by also controlling for temporal effects, since the number of occurrences of a class, patent, inventor or firm can vary over time. While some classes are popular in some years and not in others, a change in popularity ought not influence the true proximity between technology classes. For example, if a firm only worked on what was most popular every year, the firm's activity would not provide new information on how technologies are related. Controlling for temporal effects allows us to measure how unusual it is that two classes co-occurred, given \textit{when} they were each popular.

\clearpage
\section{Methods: Measuring Technology proximity \\while Controlling for Impinging Factors}

\subsection{Citations}
Using citation information in patent documents, we measured how often patents in two classes cited each other directly (Direct Citation), how often classes were cited together by the same patents (Co-Citation), how similar were the patterns of citations classes made or received from all other classes (Cosine Similarity, Inputs and Cosine Similarity, Outputs) and how similar were the patterns of citations classes made or received from all other patents (Cosine Similarity, Inputs, High Resolution and Cosine Similarity, Outputs, High Resolution). These different measures have typically been used to capture different aspects of technological proximity, as described above. However, all of the measures rely on patents' citations, and citations are determined by more than technologies' true proximity.

To clean the empirical signal of technology proximity from possible spurious relationships caused by the impinging factors, we compared the empirical proximity values to a null hypothesis: What would the measured proximity be by chance, given all the impinging factors? We calculated the random expectation by creating 1,000 randomized versions of the patent citation history, in which all of the impinging factors were exactly preserved. To create these randomized controls we identified groups of citations in which all the following properties were the same: the year the citing patents were issued, the year the cited patents were issued, and whether the citing and cited patents were in the same class (cross-class vs. same-class citations). For same-class citations, we only created citation groups in which all patents were in the same class. We then shuffled the cited patents among the citations in the group (Fig. \ref{citation_rewiring_diagram}). Perhaps surprisingly, virtually all citations were able to be grouped with other, similar citations and shuffled in this way (\textit{Appendix} \ref{Randomization_effectiveness}). The resulting shuffled versions of the network were thus different from the original, but preserved all the desired features of the number of patents in each class, the patent age sequence, etc. 

\begin{figure}[ht!]
\begin{center}
\includegraphics[width=\columnwidth]{figs/Rewiring_Histogram.pdf} 
\end{center}
\caption{\textbf{The empirical value of proximity between any two classes was compared to the distribution of proximity values across 1,000 randomized controls for that link.} Each panel represents the link between two classes, the row class (e.g. Agriculture) and the column class (e.g. Clothing). The proximity metric shown is Co-Citation, the number of patents that cited patents in both the row class and the column class. Histogram: the distribution of proximity values for that link across 1,000 randomized controls. Vertical lines: the empirical value for that link derived from the original patent citation network, colored by whether it is below, within, or above the values of the randomized controls. The empirical proximity value for a given link was typically completely outside the distribution of the randomized controls' proximity values for that link (see Fig. \ref{related_unrelated_percentages}). The panels shown are a small sample of the 7,260 possible pairs of classes in the network of 121 IPC classes.}\label{rewiring_histogram}
\end{figure}

We used the randomized patent citation history to calculate the different proximity measures between the technology classes. For each pair of classes, we obtained a histogram of measured proximity values across the 1,000 randomized controls (Fig. \ref{rewiring_histogram}, gray bars). For certain measures and conditions it is also possible to calculate the complete probability distribution for the randomized controls, using analytic approximations (\textit{Appendix} \ref{analytic_approximations}). In contrast, the numerically-generated randomized controls are valid across all conditions and all measures.

We compared the histogram of proximity values from the randomized controls to the proximity value calculated from the empirical patent citation history (Fig. \ref{rewiring_histogram}, vertical lines). For most pairs of classes the empirical proximity measure was different from all 1,000 of the randomized control values, sitting entirely outside the histogram (Fig. \ref{rewiring_histogram}, (blue and red lines). This is analogous to the empirical link having a p-value below 0.001.

\subsection{Co-Classification and Co-Occurrence}
We controlled for the impinging factors in Co-Classification and Co-Occurrence measures by again comparing the empirical data to randomized controls. We created randomized versions of the patent record in which the number of associations made by each class, patent, inventor and firm were preserved. We also preserved temporal effects by treating each year of patent data separately, randomizing each individually, then combining them into a single, randomized version of history. We created 1,000 randomized controls in this way, described in more detail in \textit{Appendix} \ref{Cooccurrence_Methods}. We again compared the proximity measures calculated from the randomized controls to those of the empirical values, as with the citations in Fig. \ref{rewiring_histogram}.


\begin{figure*}[htbp!]
\begin{center}
\includegraphics[width=.8\textwidth]{figs/Z_Score_Inflation_Direct_Citation_IPC.png} 
\end{center}
\caption{\textbf{The empirical proximity between a pair of classes can be compared to that of randomized controls as z-scores, but z-scores inflate with the number of patents in the classes, which requires a correction.} 
Solid lines: analytic model, dots: data, dashed lines: data trend.
A) The randomized controls' links' means and standard deviations, which both grew as the number of patents in the pair of classes grew, though at different rates.
B) The ratio of the randomized controls' links' means and standard deviations, which grew large as the number of patents in the pair of classes grows.
C) The empirical proximity values, compared to randomized controls using z-scores. The space of possible z-scores was limited above by the case where all patents in class $X$ cited all patents in class $Y$, and below by the case where none of the patents in class $X$ cited any patents in class $Y$. The limits of this space were modeled analytically (gray region), and the observed z-scores did grow in magnitude as the space expanded.
D) The z-scores were deflated by finding the inflation trends for the positive and negative z-scores, then dividing each z-score by the value predicted by the trend. The resulting deflated z-scores had no relationship with the number of patents in each class.
}\label{z-score_deflation}
\end{figure*}
% \clearpage

\subsection{A Normalized Measure of Technology Proximity}
For each pair of classes, we summarized the difference between the empirical proximity and the random expectation through a z-score. From the  numerically-obtained distribution of randomized controls we calculated the mean ($\mu_{random}$) and the standard deviation ($\sigma_{random}$), then expressed the empirical proximity measure ($x_{empirical}$) as a z-score: $z = (x_{empirical} - \mu_{random})/\sigma_{random}$. The z-score expressed how more or less connected the pair of classes were than would be expected by chance, given the impinging factors.

The z-score values were nearly a completely normalized proximity measure, but z-scores are still affected by an impinging factor: the number of patents in each class. The maximum and minimum possible z-score for a link between two classes grow as the number of patents in each class grows; the space of valid z-scores increases and their magnitudes ``inflate" (Fig. \ref{z-score_deflation}). As an example, consider the Direct Citation measure between two classes $X$ and $Y$. All else equal, the number of expected citations between the two classes is determined by the number of patents in each class, $X_n$ and $Y_n$; the mean of the randomized controls grows proportionally with $X_n*Y_n$ (Fig. \ref{z-score_deflation}A, blue). However, the standard deviation of the randomized controls grows with the square root of $X_n*Y_n$ (Fig. \ref{z-score_deflation}A, green), and so the mean grows large relative to the standard deviation (Fig. \ref{z-score_deflation}B). The maximum distance an empirical value can be from the mean is also proportional to $X_n*Y_n$ (Fig. \ref{z-score_deflation}C, gray region): the minimum empirical value is always 0, and so the largest negative deviation from the mean is just the value of the mean; the maximum number of possible citations from $X$ to $Y$ is if every patent in $X$ cited every patent in the other, which is $X_n*Y_n$ itself and thus clearly proportional to $X_n*Y_n$. The links between large classes thus had a larger space of possible z-scores they could have, and indeed the observed z-scores grew in magnitude as the number of patents increased (Fig. \ref{z-score_deflation}C, dots).

The inflation of z-scores is not an empirical result, but an analytical relationship. It arises from three steps:
\begin{enumerate}
\item Randomized controls' mean value and standard deviation grow at different rates as the number of patents in a pair of classes increases, so their ratio increases.
\item The difference between the empirical value and the randomized controls' mean value (the numerator of the z-score) has a maximum possible value, and that value grows proportionally with the number of patents in both classes. 
\item As the maximum value of the numerator of the z-score grows, the denominator (the standard deviation) grows less quickly. The numerator grows large relative to the denominator, larger z-scores are possible, and the space of possible z-score values inflates.
\end{enumerate}

To illustrate that z-score inflation is definitional, we estimated analytically how the means and standard deviations of the randomized controls' link values would grow with $X_n*Y_n$ (Fig. \ref{z-score_deflation}A, B, solid lines; \ref{z-score_deflation}C, gray region). The analytic model is only illustrative, as the statistics of the randomized controls are due to a variety of factors and could thus deviate from the simple model. This is why some of the z-scores in Fig. \ref{z-score_deflation}C are actually below the lower barrier modeled, outside the gray region; the randomized controls' standard deviation was smaller expected, so the z-score's magnitude was larger than would otherwise be possible.

We corrected for the z-score inflation with a simple heuristic: regressing out the inflation trend. We calculated the trend of the z-score inflation for the positive and negative z-scores, then divided the z-score values by the trend line (Fig. \ref{z-score_deflation}C, D, dashed lines). We calculated the trend and regressed it out for each measure of proximity individually (Figures in \textit{Supporting Information}). The z-scores were thus deflated, and so the normalized measures of proximity had no correlation with class' number of patents.

The deflated z-scores were the desired measure of technological  proximity that was normalized to remove impinging factors. Empirical and normalized proximity measures could convey very different perspectives. For example, the empirical number of citations from ``Medical \& Hygiene" to ``Electric Communication" seemed large at $17,542$ citations, which put it in the top 3\% of citations. However, this was actually fewer citations than would be expected by chance: the randomized histories had $63,826 \pm 161$ citations between the two classes, producing a z-score of $-288.32$. After z-score deflation the normalized proximity was $-2.56$, in the bottom 3\% of normalized values.


\subsection{Robustness of Analysis to Different Classification Systems}
All patents were classified by patent agents under two systems: the United States Classification system (USPC: 430 classes) and the International Patent Classification system (low-resolution IPC3: 121 classes, high-resolution IPC4: 629 sub-classes). Data of patents’ main classes for all classification systems were available for patents from 1976-2010, and data from those years are used for all the relevant proximity measures. USPC secondary classification was available for patents from 1976-2010, and so Co-Classification using USPC reflect the same years of data as the other measures. For IPC3 and IPC4 data on secondary classifications was only available for patents from 1976-2006, and so the Co-Classification measure using these classifications reflect 4 fewer years of data.

The US patent office recently joined other national patent offices to exclusively use the new Cooperative Patent Classification system (CPC), which is based on the IPC. In order to ensure our findings are the most relevant for the future, we focus here on results from the IPC3, which is more similar to the modern CPC system than USPC and is more easily visualized than IPC4. We also repeated the analysis using the USPC and the higher-resolution IPC4 classification systems and found qualitatively similar results to those shown here (\textit{Appendix}).

\subsection{Data and Code for Reproduction and Extension of these Methods}
All code to perform these analyses and produce these figures is included online at \href{https://www.github.com/jeffalstott/technologyspace}. This code takes as input a set of raw data files describing patents’ classifications, authors, assignees and citations. These raw data files are in \textit{Supporting Information}. The code is written in Python and includes documentation.

\section{Results}

\subsection{Normalization Creates Closer Correlation of Different Measures of Technology Proximity}

Normalization changed how the different kinds of proximity measures compared to each other. Among the empirical networks (before normalization) there were three groups of correlated networks (Fig. \ref{measure_correlations}, lower left panel). In the first group were Direct Citation and Co-Citation, in the second were the four varieties of Cosine Similarity, and last was Co\hyp{}Classification.
After normalization, all of the proximity measures became more correlated with each other measure (Fig. \ref{measure_correlations}, upper right panel). For classification with IPC3 (Fig. \ref{measure_correlations}) and IPC4 (Fig. \ref{Network_Correlations_Linear_IPC4}), Co-Classification was less correlated with other measures, though its correlation was also raised after normalization\footnote{The Co-Classification network for IPC3 and IPC4 only included the available patent data from 1976 to 2010, unlike the other proximity measures. However, similarly restricting the other proximity measures to data only from 1976 to 2006 shows the same lower correlation for Co-Classification, and the same overall correlation structure across all measures (Fig. \ref{Network_Correlations_Linear_2006_IPC}).}. In contrast, Co-Classification was as correlated as other measures when using classification with USPC; the less-correlated measure with USPC was Co-Citation (Fig. \ref{Network_Correlations_Linear_USPC}).


\begin{figure}[]
\begin{center}
\includegraphics[width=\columnwidth]{figs/Network_Correlations_Linear_IPC.png}
\end{center}
\caption{\textbf{Normalizing networks made technology proximity networks more similar to each other, while less influenced by other factors.} Correlations are between the link weights of technology networks created with different measures of proximity. 
Lower left) Empirical networks.
Upper right) Normalized networks. Normalized networks' link weights are z-scores, where a link value of the empirical network is expressed as a z-score of the randomized controls' values for that link. Scatter plots and heat maps of the raw data for all comparisons of proximity measures are in Figs. \ref{Measure_Comparison_Scatter_Plots} and \ref{Measure_Comparison_Hex_Plots}.
}\label{measure_correlations}
\end{figure}

Thus, removing impinging factors led to more agreement among the different measures of proximity. There is little \textit{a priori} reason to expect that normalization would lead to increased agreement, though we discuss possible reasons below. However, \textit{a posteriori} the increased agreement of different proximity measures validates the utility of the method of normalizing out impinging factors.

\subsection{The Technology Network is Sparse}
Before normalizing proximity measures, it is difficult to quantify the distance between two technologies and then assert if the resulting number is a high or low value. However, comparing an empirical link weight to randomized controls yields a natural interpretation for whether a link is particularly strong or weak: an empirical link weight is stronger or weaker \textit{than would be expected by chance.} Most links in the networks were weaker than would be expected by chance (Fig. \ref{related_unrelated_percentages}), measured as being below the link weights of any of the 1,000 randomized controls (analogous to $p<.001$). For most proximity measures, \textasciitilde{}20\% of the links were stronger than chance, indicating the two technology classes they connected were particularly related. 


\begin{figure}[b]
\begin{center}
\includegraphics[width=\columnwidth]{figs/Related_Unrelated_Percentages_Two_Column_IPC.pdf} 
\end{center}
\caption{\textbf{Few links between classes had a higher proximity than that expected by chance.} Technology networks created using different measres were compared to 1,000 randomized controls, by comparing the weights of their links. For most networks, a majority of the empirical links had lower proximity than any of the randomized controls (blue), and a minority were above the randomized controls (red). The exception was Co-Occurrence, Firm networks, for which many more pairs of technology classes had greater Co-Occurrence than would be expected by chance.
}\label{related_unrelated_percentages}
\end{figure}

The technology networks were thus sparsely connected, though sparse is a relative term; the network had 7,260 possible links, and with a link density 20\% there were still 1,452 links remaining. However, using other classification systems with higher resolution revealed an even sparser network: using the USPC classification (430 classes) yielded a sparsity of \textasciitilde{}10\%, and using the IPC4 classification (629 classes) yielded a sparsity of \textasciitilde{}5\% (Figs. \ref{related_unrelated_percentage_USPC}, \ref{related_unrelated_percentage_IPC4}).

Controlling for spurious factors quantifies an intuitive fact: most technologies are not particularly proximate to each other. Instead, any single technology is only notably proximate to a fraction of the other technology domains. This finding on the technology map structure again justifies the value of our method of normalizing out impinging factors.

Sparsity was notably less the case for the Co-Occurrence measured from firms' patenting histories: technology pairs occurred together in firms' patenting histories at rates greater than chance about twice as often as in the other networks. Using the IPC3 classification system this meant \textit{most} technology pairs were significantly connected. However, using the higher resolution USPC or IPC4 classifications lowered the frequency of significant technology pairs to \textasciitilde{}20\% (Figs. \ref{related_unrelated_percentage_USPC}, \ref{related_unrelated_percentage_IPC4}).

We also analyzed the patenting histories of countries and found a similar pattern to that of firms, though the comparatively small sample of $<200$ countries meant the trend was not statistically significant (\textit{Appendix} \ref{cooccurrence_country}). 

\subsection{Inventors' Behavior Follows Proximity Measures Closely, While Firms' Portfolios Follow Less Closely}
Inventors' patenting histories closely followed the technology network structure identified by the normalized measures. Pairs of technology classes' normalized rates of Co-Occurrence in inventors' patent histories were strongly correlated with the other citation- and classification-based networks (Fig. \ref{Relatedness_Behavior_Correlation}, blue bars). The normalized technology networks, then, not only began to converge on a common description of technologies' proximity to each other, but to a description that also mirrored inventors' behavior. The technology network maps may thus provide explanation for why a single mind that is able to invent in ``organic chemistry" is also likely able to invent in ``agriculture": these technology domains are intrinsically related. Thus, this normalized map will be particularly useful for analyzing and predicting the invention portfolios and learning paths of inventors.

Firms' patent portfolios, in contrast, followed the other technology networks less closely. Pairs of technology classes' normalized rates of Co-Occurrence in firms' patent histories were also correlated with the other networks, but only modestly (Fig. \ref{Relatedness_Behavior_Correlation}, green bars). The association between technology proximity and inventive behavior is similar for firms and inventors, but the variance is much larger for firms, reducing the strength of the association (Fig. \ref{Measure_Comparison_Scatter_Plots}). Firms, then, are like inventors in that they tend to invent in classes related to those that they already have experience in; notwithstanding, deviations from this general pattern are much more common and sizable for firms. Previous research has used co-occurrence data to investigate whether firms preferentially diversify into related classes \cite{Bottazzi2010, Breschi2003, Teece1994}. The present results show that firms' patent portfolios are indeed influenced by the proximity of different technology domains, but this is just one, modest influence. Firms' decisions to enter into a new technology domain are also determined by such factors as market demand, the availability of capital and risk diversification. Furthermore, firms are less constrained than individual inventors; they can hire additional staff or acquire new ventures that can bring in new knowledge unrelated or dissimilar to a firm's previous capabilities. As such, firms' inventive behavior only partially reflects other measures of the technology proximity space. 

\begin{figure}[tbp]
\begin{center}
\includegraphics[width=\columnwidth]{figs/Relatedness_Behavior_Correlation_IPC.pdf} 
\end{center}
\caption{\textbf{Inventors closely followed the technology network maps derived from the normalized proximity measures, while firms followed the maps less closely.} The normalized counts of how often two technology classes co-occurred in inventors' patenting history correlated with other normalized measures of technology proximity (blue bars). The normalized counts of how often two technology classes co-occurred in firms' patent portfolios correlated only modestly with the other measures of proximity (green bars).
}\label{Relatedness_Behavior_Correlation}
\end{figure}

\subsection{Network Stability Over Time}
The technology networks presented so far were constructed using all available patent data from 1976 to 2010. These networks represent the ''view from 2010". How much does the network change if we consider the view from different points in history? We previously analyzed the temporal stability of the unnormalized technology proximity measures, and found that they altered little over time\cite{Yan2015}. Here we analyzed the temporal stability of the normalized technology networks by constructing multiple versions using data up year $X$, where $X$ is each year from 1976 to 2010. We assessed the similarity of the networks over time, again by measuring the correlation of their link weights. We measured the correlation of a network created with data from each year X with the network created with data from year $X-1$.

\begin{figure*}[htbp!]
\centering
\includegraphics[width=.8\textwidth]{figs/Network_Temporal_Stability_IPC.pdf} 
\caption{\textbf{All measures of technology proximity were stable over time.} Lines: the correlation of the link weights of a technology proximity network, calculated with data from 1976 to year $X$, with the link weights of the same network calculated with data from 1976 to year $X-1$. Each panel corresponds to a different measure of technology proximity. Blue lines: the empirical technology proximity. Green lines: the normalized measure of technology proximity. 
}\label{Network_Temporal_Stability}
\end{figure*}

The networks started out unstable in the 1970s, but quickly became stable by the 1980s and remained so through 2010 (Fig. \ref{Network_Temporal_Stability}). The initial instability was likely due to comparatively little data in these first few years. With adequate data, however, the normalized networks are generally stable over time. Thus, the technology proximities calculated here are not particular to the year 2010, but reflect a more lasting set of relationships. These relationships may of course still change slowly or locally, and large changes can not be ruled out before 1976 or after 2010.

\section{Discussion} 
Normalizing technology network maps by controlling for impinging factors uncovers previously obscured information, such as the sparsity of the space of technology proximity. Normalization also leads to convergence of many of the different measures of proximity, as seen through their increased correlation. These results validate the method of normalizing proximity measures to remove impinging factors and the usefulness of resulting maps. 

\subsection{Technology Proximity, the Inventive Process, and Technology Development}
After normalization, different technology proximity measures began to provide a similar map of the technology space. There is no \textit{a priori} reason why this should happen. In fact, one could assume that proximity measures based on different perspectives should lead to different maps of the technology space. However, the increased agreement of the different measures could be explained by that the simple relationship of inventive inputs, inventive outputs, and inventive processes becomes more apparent after impinging factors and their spurious effects are removed.


Let us assume that inventions are the result of a cognitive process, $f()$, that transforms knowledge inputs $x$ into inventive outputs: $y=f(x)$. The following then holds: $f_1(x_1)=f_2(x_2′)$ if $f_1=f_2′$ and $x_1=x_2′$. Two identical inventive processes that are given the same inputs will yield the same outputs. We can then relax the concept of equality to just similarity: similar inventive processes that are given similar inputs will yield similar outputs. We can then explain why technology classes that frequently Co-Occur in inventor's portfolios also have similar sets of inputs or outputs: technology classes that require similar inventive processes (``functions") are being used on similar inputs to obtain similar outputs. Inventors with similar inventive process functions $f$ are using similar inputs $x$ and thus getting similar outputs $y$, or seeking to create similar outputs $y$ and thus are using similar inputs $x$. Different measures of technology proximity that measure input similarity vs. output similarity thus correlate, because inputs and outputs are linked by the function that turns inputs into outputs.

Using normalized measures of technology proximity, we now better understand the statistics of how inventors use their knowledge to create new inventions. The maps presented here are a cleaned signal of technology proximity, which we can now use to address further questions about how technology proximity affects technology development on a large scale. For instance, it has been hypothesized that the growth or decay of one technology domain is affected by technology dynamics and competition in neighboring domains \cite{Saviotti1995,Bruckner1994} and that technology development in one area benefits from knowledge spillovers induced by R\&D investments in proximate technologies \cite{Verspagen1997,Verspagen1997a,Bloom2013}. The normalized network maps can be used to test these hypotheses, and so may improve our ability to explain specific classes’ number of patents, or how proximate technology domains interact and even compete. If the network maps can be used to explain domains’ dynamic coupling, then the maps could be used for prediction: if a new development arises in one domain, we could predict cascading effects reaching into proximate domains.

\subsection{Absolutely Related vs. Particularly Related}
It is important to highlight that the methods introduced here do not measure if two technological domains are proximate in an absolute sense; they only measure if two classes are particularly proximate. As an example, consider a class with just one patent that is super-connected, which cites every other patent. Randomizing this citation history while also preserving each patents' outward and inward citations would yield the exact same arrangement: the super-connected patent would still cite every other patent. The randomized controls would not be able to deviate from the observed data, because only the original arrangement of citations satisfies the conditions. The empirical links between the super-connected class and other classes would look exactly the same as the randomized controls' links, and so the z-scores for those links would be 0. The class with the super-connected patent could thus reasonably be considered very proximate to \textit{every} other class, but the methods we introduce here would just see it as not particularly proximate to \textit{any} other class. This example illustrates that we do not measure if two technology classes are proximate in an \textit{absolute} sense, but only if, given their absolute level of connection to all other classes, they have \textit{particular} connectivity or disconnectivity to each other above what could be expected by chance.

\subsection{Alternative Definition of Impinging Factors}
We identified several aspects of the patent record as not pertaining to the proximity of technological artifacts, arising instead through the patenting or inventive process. We called these aspects of the patent record impinging factors, because they affect measures of technological proximity without contributing to the true signal of proximity. These impinging factors included patents' number of citations, the age of the cited patents, the number of technology classes an inventor or firm has patented in, etc.; we described above our assessments of what drives these factors. However, the methods that we present here could be modified to control for additional impinging factors, such as whether inventors' patents' preferentially cite patents by other inventors in the same city or firm. It is also possible to control for fewer factors, if a researcher wanted those factors to affect the measure of proximity. The general method simply requires clearly identifying which properties of the patent record are to be controlled for, then generating randomized controls that preserve those properties.

\subsection{Utility of the Maps and Mapping Technique}
The normalized technology networks measured here can now serve as map for uses in technology development planning and management. Both individual inventors and firms can locate themselves and their knowledge on the map and observe what technology domains are nearby in the technology space. Nearby domains are likely easier targets for new invention over more distant domains. Inventors are particularly justified in using the map to guide their future inventions, since inventors who successfully patent in multiple domains typically do so in proximate domains. Firms are less justified in restricting themselves to targeting technologies in solely those domains that are proximate to their existing knowledge base; they may instead hire additional inventors with new knowledge to roam further afield. For both inventors and firms, it may be possible to use the map to plan a long-term research and development path: starting in the domain where one currently has knowledge, one can target at a series of domains that are always proximate to each other, but ultimately result in patenting in a domain very unrelated from one's origin. Thus, the normalized technology proximity map can be a significant strategic planning tool.

Strategies of following the map (or not) are justified if an individual or firm wants to behave like those who successfully patent. It is possible, however, to have a higher bar: to be an inventor whose patents receive many citations, or to be a firm whose inventions yield high financial returns. Multiple lines of research have found that inventive efforts that combine or move between proximate technologies are more likely to successfully invent, but the results are low novelty and unlikely to be a breakthrough with high impact \cite{Fleming2001,Fleming2007, Simonton1999,Chan2011,Chan2015}. In contrast, inventive efforts that combine distant technologies are less likely to succeed in creating an invention, but if this hurdle is overcome then the results are more novel and more likely to be a breakthrough. The network maps presented here provide a cleaner measure of technology proximity to further test this theory: it is possible to identify inventors and organizations that persistently followed the map versus not, and then examine their performance. Analyzing inventive behavior in terms of a network may also reveal more complexity: high-performing inventors or firms may indeed follow the network more or less closely, but they may also employ more complex strategies like targeting particularly dense or sparse regions of the network.

Additionally, the normalization methods presented here are also of potential relevance in other domains, such as the study of the proximity of scientific fields. The same randomization approaches can be used to map the latent space of scientific disciplines using journal articles' citations, classifications, etc. Measuring the space of scientific topics with greater accuracy and statistical rigor may allow for answering such questions as whether the processes of intellectual creation follow universal rules, regardless of the scientific or technological nature of the knowledge involved.

\subsection{Conclusion}
Technology is a complex system, but we can gain understanding of that system by mapping out its components and their relations to each other. With the more accurate patent-based technology mapping techniques presented here it is possible to study technology development with a new level of clarity, including both aspects of technologies themselves and how humans interact with those technologies. Improved understanding of technologies and invention may ultimately inform better technology development policies, leading to more successful technology innovation and management.

\begin{acknowledgements}
JA, GT and JL designed the study. BY collected the data and visualized the networks. JA and GT developed and performed the analysis. JA, GT and JL wrote the paper. We thank Ulf Bissbort, Bikramjit Das, Tommaso Demarie, Francois Lafond, and Chris Magee for helpful discussions. This work was supported by the SUTD-MIT International Design Centre (IDG31300112) and by the Singapore Ministry of Education Tier 2 Academic Research Grants (T2MOE1403).
\end{acknowledgements}

% \newpage
% \beginappendix
% \pagebreak
% \clearpage

\newpage
\begin{center}
\textbf{\large Appendix}
\end{center}


\section{Network Visualization}\label{network_visualization}
We visualized the empirical and normalized technology proximity networks
(Fig. \ref{Empirical_Normalized_Networks}A-B). We highlighted a community structure for each network, which was calculated by approximately maximizing the weighted modularity using a faster version of the Louvain method \cite{Newman2004, Blondel2008, Traag2015}. Only a subgraph of the networks' links were visualized: the planar maximally filtered graphs \cite{Tumminello2005}. These graphs contained the set of links with the highest weights that were also topologically planar, such that they could be laid out flat on a plane without links crossing.

\section{Effectiveness of the patent citation network randomization}\label{Randomization_effectiveness}
\begin{figure}[h]
\begin{center}
\includegraphics[width=\columnwidth]{figs/citation_rewiring_diagram_one_column.png} 
\end{center}
\caption{\textbf{The citation rewiring process used to create randomized control networks preserved many properties of the original patent citation network.} Citations were selected whose citing patents were issued in the same year and whose cited patents were also issued in the same year. Groups of citations were selected that were either all cross-class (left) or all same-class and all within the same class (right). The citations in the group were then shuffled. Performing this shuffling operation resulted in a randomized version of the patent citation network that still preserved many properties of the original networks, such as the cross-class citation rates, the time lag of citations, and the number of citations made and received by each patent. }\label{citation_rewiring_diagram}
\end{figure}

There are two wrinkles in how the generation of the randomized controls, which could in theory could affect the normalized proximity measures, though in practice do not. The first wrinkle is that it is not guaranteed that for every citation there is another citation to be paired with that has all the same properties required. Fortunately, this happened rarely. Each citation was part of group of citations that had the same citing patent year, cited patent year, and cross-class identification (and for same-class citations, being within a particular technology class). Fig. \ref{rewiring_groups} shows the number of citations in each group that was represented in the patent network. Only approximately 14.16\% of citations were part of a group that fewer than 10 members. 2.55\% of citations were part of a group with 1 member; these were unique citations, and could not be rewired. Leaving these citations unaltered made the randomized control networks more similar to the empirical network. As discussed in the main text, the randomized control networks and the empirical network were still very different, and so the effect of the unrewired links was unappreciable.

\begin{figure}[h]
\centering
\includegraphics[width=\columnwidth]{figs/Citation_Group_Sizes.pdf} 
\caption{\textbf{Most citations had many other citations with the same properties, and so could be rewired.} The rewiring procedure used to create randomized controls required pairing each citation with another citation with similar properties (Fig. \ref{citation_rewiring_diagram}). If the group of citations with those properties was small, then that link would not have many opportunities for rewiring, and so the randomized controls would be similar to the empirical network. Over 85\% of citations were in a group that had 10 or more members. Only 2.55\% of citations were unique and could not be rewired. In practice, the empirical networks rarely resembled the randomized controls (Fig. \ref{related_unrelated_percentages})}\label{rewiring_groups}
\end{figure}

The second wrinkle of this normalization method is with rewiring cross-class citations. As in Fig. \ref{citation_rewiring_diagram}, if all four patents are from different classes, the desired outcome is achieved. However, it is possible that the citing patent of one citation is in the same class as the cited patent of the \textit{other} citation. In this case, both citations are indeed cross-class citations, but after rewiring one of the citations would become a same-class citation. Thus, the cross-class citation rate would decrease in the randomized controls, and the same-class citation rate would decrease (Note that it is not possible for the reverse mistake to occur, because all the same-class citations are paired and swapped separately.). The solution, of course, is to check that the paired citations do not have the problematic arrangement of classes, and so will not create a same-class citation. While this works in principle, it does not in practice. The solution requires checking, rejecting, and re-suggesting possible pairs of citations. This process creates significant computational problems, and it is hard to assess if and when the process will even converge. Because of this, we left the problem in place, and so the randomized controls had an increased rate of same-class citations. This rate was small, with the rate of same-class citations raising from 39.74\% in the empirical patent citation network to a typical rate of 41\% in randomized controls. The decrease in cross-class citations in the randomized control patent citation networks would typically make the empirical network appear to have an unusually high amount of citations between two classes, leading to an unusually strong connection in the technology network. However, as discussed in the main text, the empirical network generally had much lower proximity values than would be expected by chance. As such, the error that the imperfect rewiring introduces would only make the unusually low normalized values more notable.


\begin{figure*}[]
\centering
\includegraphics[width=\textwidth]{figs/Measure_Comparison_Scatter_Plots_IPC.png} 
\caption{\textbf{Comparison of link weights across different proximity measures, as scatter plots.} Blue: empirical networks. Red: normalized networks. Black: empirical vs. normalized link weight for each measure compared to itself (x-axis: empirical, y-axis: normalized). Text: Pearson correlation.
}\label{Measure_Comparison_Scatter_Plots}
\end{figure*}

\begin{figure*}[]
\centering
\includegraphics[width=\textwidth]{figs/Measure_Comparison_Hex_Plots_IPC.png} 
\caption{\textbf{Comparison of link weights across different proximity measures, as heat maps.} As Fig. \ref{Measure_Comparison_Scatter_Plots}, but expressed a heat map of point density. Blue: empirical networks. Red: normalized networks. Black: empirical vs. normalized link weight for each measure compared to itself (x-axis: empirical, y-axis: normalized). Text: Pearson correlation.
}\label{Measure_Comparison_Hex_Plots}
\end{figure*}

\section{Randomization of Co-Classification and Co-Occurrence Data}\label{Cooccurrence_Methods}
 The process for controlling for the number of occurrences of each class and the number of associations of each patent, inventor or firm is the same principle as used in the patent citation network: many randomized control versions of the empirical data are created.  In the randomized controls, the number of occurrences of each class and the number of associations of each patent, inventor or firm is preserved, but the assigning of technology classes to patents, inventors or firms is otherwise random. This goal is practically accomplished by expressing the co-classification or co-occurrence data as a bipartite graph, in which technology classes are one type of node and they form connections to another type of node, be that patents, inventors or firms. Randomized controls are then created by shuffling the bipartite network's links, but preserving each node's degree. The bipartite network is then projected into a single-mode network, the co-classification or co-occurrence network. The resulting one-mode network is then compared to the same information from the empirical, unrandomized data.

The bipartite network is  equivalent to a binary matrix, with patents, inventors or firms forming the rows and technology classes forming the columns. Reshuffling the bipartite network is equivalent to creating random versions of the binary matrix, with each row and column having the same sum. Bottazzi and Pirino described how creating random controls in this way for co-occurrence data can markedly alter the interpretation of empirical co-occurrences \cite{Bottazzi2010}. We extend the shuffling technique they used with a reshuffling method designed with bipartite networks in mind \cite{Gobbi2014}. These methods analytically determine the number of rewires necessary to make on the original bipartite network in order to effectively take an unbiased sample from the set of networks with the same degree sequence. We used the BiRewire software package to first calculate the necessary number of rewires for each of the bipartite networks we examined, and then to rapidly execute the rewires \cite{Gobbi2015}.

We created randomized controls that preserved temporal changes in class' popularity and inventors' or firms' activity by treating each year of data as a separate bipartite network. Each year was rewired independently, and then all years were combined to create the final randomized control to which the empirical network was compared.

Preserving temporal behavior introduces information which is not typically considered in the analysis of co-occurrence data. It also introduces additional complexity. Consider an inventor who was active in one technology class: \textit{Hats}. This inventor was active for 10 years, patenting each year, each time in the technology class for \textit{Hats}. If we identify this inventor as ``occurring" in \textit{Hats} each year, and randomize each of the 10 years individually, it is likely that across the 10 randomized years the inventor will then occur in  many different technology classes. When we combine the 10 randomized years back together, we would then observe that the inventor occurred in many technology classes, perhaps 10, which is far more than the 1 class in which the inventor was actually active. By marking the inventor as ``occurring" in every year individually, our randomization will thus break the  basic task of preserving the number of classes the inventor was active in. Therefore, any time an inventor or firm is awarded patents in a class in multiple years, we have a problem.

The solution to the problem of an inventor or firm ``occurring" over multiple years is to not consider the entity as occurring over multiple years. Instead, the inventor or firm is considered to occur in each class only once, in a single year. In this way, randomizing each year individually cannot increase the total number of classes that an entity associates with in its history. After randomization of individual years and combining them together, the number of classes per entity and entities per class will still be preserved. 

For the purpose of inventors or firms patenting in technology classes, the most salient year to mark the entity as occurring in a class is the \textit{first} year that entity patented in that class. This is particularly relevant for controlling for phenomena like popularity-chasing; if a firm only enters technology domains because they are popular, that does not provide more information about how related technology domains are. We thus mark each inventor or firm as occurring in a technology class when they first entered into that class, and compare to randomized controls that preserve the timing of the entries.

\subsection{Preserving Temporal Information Markedly Affected Firm Measures, but not Others}
Preserving the year sequence in the randomized controls had only a modest effect on the measured normalized network, for most measures (Fig. \ref{CoOccurrence_Year_Preservation_Comparison}). However, the Co-Occurrence, Firm measure was markedly altered by preserving the year sequences. Without preserving year sequences, the Co-Occurrence, Firm network had \textasciitilde{}25\% of its links stronger than random chance, closer to the fraction observed in the other proximity measures. By preserving the year sequences in the randomized controls and normalizing out such phenomena as popularity-chasing, the normalized Firm, Co-Occurrence network had \textasciitilde{}50\% of links stronger than chance.

\begin{figure}[htbp!]
\centering
\includegraphics[width=\columnwidth]{figs/CoOccurrence_Year_Preservation_Comparison_IPC.pdf} 
\caption{\textbf{Most proximity measures were only modestly affected by preserving yearly history.} The exception was in Co-Occurrence, Firm, which showed a marked change in the number of links that were stronger than chance.}\label{CoOccurrence_Year_Preservation_Comparison}
\end{figure}

By preserving temporal effects, the Co-Occurrence of technology classes in firms' patent portfolios were found to be generally more frequent than chance. Thus, by creating randomized controls that had more features in \textit{common} with the empirical data (the temporal sequence), the empirical data appeared more \textit{unusual}. This may seem counter intuitive, and so we provide some intuition here. Consider two statements:
\begin{quote}
1. ``I am a human, and I speak Mandarin Chinese". 
\end{quote}
This is unusual, but not that unusual. A randomly selected human has about a 1/7 chance of speaking Mandarin Chinese.

\begin{quote}
2. ``I am an Italian human, and I speak Mandarin Chinese".
\end{quote}
This is very unusual. A randomly selected Italian human has a much lower chance of speaking Mandarin Chinese.

Thus, by adding additional constraints to the randomized controls, we generate controls that are more like the empirical sample (human vs. Italian human), but the empirical sample is now more different from the controls.

In our case, we generate randomized controls that can either:
\begin{enumerate}
\item freely associate classes with firms, regardless of firms' histories
\item must associate classes with firms only when the firms entered a new class
\end{enumerate}

Consider a class, \textit{Hats}, that had some level of popularity $P$ across all of history, but during some periods of history had a much smaller popularity, $p$. Using method 1, randomized controls will match up a firm with \textit{Hats} at the rate $P$. However, it is possible that a specific firm only entered a new class at the moment in history when \textit{Hats} had the diminished popularity $p$.  Method 1 is blind to this fact. However, using method 2, randomized controls will match up the firm with \textit{Hats} at the rate $p$. The specific firm's entry into \textit{Hats}, then, appears more unusual using method 2 than method 1, because $p<P$. 

Therefore, using randomized controls that preserve the yearly sequence of firms' entries can identify temporal effects that make a firm's movement into a class appear more unusual. This method can then be scaled to a whole population of firms, to determine if their movements in aggregate are unusual. We can then measure whether the co-occurrence of two classes in firms' portfolios is unusual, i.e. different from that expected by chance.


\section{Co-Occurrence, Country Data}\label{cooccurrence_country}
The empirical proximity links between technology classes had values typically much higher or lower than all 1,000 randomized controls, across all measures of proximity (Fig. \ref{related_unrelated_percentages}). We measure this phenomena in more detail by expressing each empirical link's value as a percentile rank, relative to the randomized controls. Fig. \ref{p_value_histograms} shows the histograms of the empirical links' ranks, for each proximity measure. For the nine proximity measures reported in the main text, the majority of links were lower than all randomized controls (rank 0) or higher (rank 100). For eight of the proximity measures, rank 0 links outnumbered rank 100 links. The exception was the Co-Occurrence, Firm network, in which rank 100 links were more common than rank 0 links.

We analyzed the patenting histories of countries to create a Co-Occurrence, Countries network, analogous to the networks created from Co-Occurrence, Inventor and Co-Occurrence, Firm measures. The country data was similar to the firm data, in that rank 100 links were more common than rank 0 data (Fig. \ref{p_value_histograms}, lower right panel). However, the vast majority of links between technology domains were between ranks 0 and 100, meaning they had values within the range expected by chance (covered by the 1,000 randomized controls). It is possible that with additional country data these links would prove to be significantly different from chance. However, with less than 200 countries, the co-occurrence data did not provide a sufficiently strong signal to assert that country's invention portfolios combined many technology classes at rates different from random chance.

\begin{figure}[htbp!]
\centering
\includegraphics[width=\columnwidth]{figs/Country_CoOccurence_Histograms_IPC.pdf} 
\caption{\textbf{All proximity measures found that most technology proximity links were very different from randomized controls, except Co-Occurrence, Country.}}\label{p_value_histograms}
\end{figure}


\section{Analytic Approximations of Randomized Controls}\label{analytic_approximations}
\subsection{Expected Number of Citations}
The expected value of citations between any pair of classes and its standard deviation can be conveniently approximated analytically by exploiting the statistical properties of our randomization process. The process can be seen as a sum of random variables $X_{t,lag}$, one for each citing-cited year ${t,lag}$ pair that describe the possible relationship between a given citing and cited class. The citation swapping procedure can be described as sampling a number of citations $n_{citing_{t,lag}}$ without replacement out of a population $N$ of \textit{swappable} citations that respect the required constraints, in which there are exactly $K$ citations directed toward the given cited class. Therefore, for each citing-cited year pair ${t,lag}$, the expected number of citations between a citing and a cited class behaves like a hypergeometric random variable $X_{t,lag}$. As such, the total expected number of citations for a given pair of citing-cited classes is described by the sum ${C_{citing,cited}}$ of hypergeometric random variables ${X_{t,lag}}$ with different number of trials \textit{n}, population size $N_{t,lag}$ and number of successes $K_{cited_{t,lag}}$. It follows that the expected value $E(C_{citing,cited})$ is approximately equal to

\begin{multline}
E(C_{citing,cited}) \sim \sum_{\forall t \in T_{citing}}\sum_{lag=0}^{t-1976} \bigl[ n_{citing_{t,lag}} \\
* p(connection)_{cited_{t,lag}} \bigr]
\end{multline}\label{expected_value_citation}


Where $n_{citing_{t,lag}}$ is the number of citations made by the given citing class to be reshuffled for each random variable $X_{t,lag}$ (i.e. the number of trials). The probability of swapping any of these citations with anyone connecting to a patent belonging to the given cited class is

\begin{equation}
p(connection)_{cited_{t,lag}} = \frac{K_{cited_{t,lag}}}{N_{t,lag}}
\end{equation}\label{probability_connection}

More specifically, for each citing-cited year pair ${t,lag}$ the number of trials $n_{citing_{t,lag}}$ is equal to

\begin{equation}
n_{citing_{t,lag}} = C_{citing_{t}} p(lag)_{citing_{t,lag}} p(outward)_{citing_{t,lag}}
\end{equation}\label{number_of_trials}

Where $C_{citing_{t}}$ is the number of citations made by patents granted in year \textit{t} belonging to the given citing class, $p(lag)_{citing_{t,lag}}$ is the probability that they cite patents granted in the year ${t-lag}$ and $p(outward)_{citing_{t,lag}}$ is the probability that they cite patents belonging to a class different from the one of origin. The latter two are indexed by $citing$, $t$ and $lag$ because, as we have shown in the panels of Fig. \ref{Empirical_Normalized_Networks}, there is a large variability across classes and time. It follows that the standard deviation $\sigma_{citing,cited}$ of ${C_{citing,cited}}$ is approximately equal to

\begin{multline}\label{standard_deviation_citation}
\sigma_{citing,cited} \sim 
\sqrt{ 
\sum_{\forall t \in T_{citing}}\sum_{lag=0}^{t-1976} \bigl[ n_{citing_{t,lag}}} \\
\shoveright{* p(connection)_{cited_{t,lag}}} \\
\shoveright{* (1-p(connection)_{cited_{t,lag}})} \\
* \frac{N_{t,lag}-n_{citing_{t,lag}}}{N_{t,lag}-1} \bigr]  
% } 
\end{multline}

When $N_{t,lag}$ is large and $n_{t,lag}$ is small compared to it, then the fraction in equation \ref{standard_deviation_citation} approaches unity. Therefore, the $\sigma_{citing,cited}$ can be approximated by the standard deviation of a binomial distribution. This is particularly handy if one would like to have an analytic solution for the p-values of the empirical proximity. In fact, the distribution of the sum of hypergeometric random variables with varying number of trials and probability of success has no closed form solution. However, the sum of binomial random variables with different $n$ and $p$, can be seen as the the sum of Bernoulli random variables with different probabilities and is, therefore, described by the Poisson binomial distribution (a.k.a. Bernoulli-Poisson distribution) \cite{Hong2013, Fernandez2010, Chen1997}. Recently, it has been shown that the Poisson-binomial cumulative and probability distribution functions have exact closed-form solutions and accurate refined normal approximations \cite{Hong2013}. Based on the equations discussed here it is straightforward to derive the expected value of Co-Citation and Cosine Similarities between classes by using the joint probability distribution of citations from patents to classes and the cosine value of the vectors of expected received citations for any given class pairs.
 
\subsection{Nature and Quality of the Analytic Approximation}
The solutions for $E(C_{citing,cited})$ and $\sigma_{citing,cited}$ reported above are excellent approximations of numeric solutions  for the number of citations between classes, as provided by our randomization process (Fig. \ref{Comparison_ANAvsNUM}). The same approach could be applied to predict the numeric solution of the expected value and variance of co-occurrences of classes in patents, inventors' and firms' patenting histories. In this case $n$ would be the number of classes in which a patent, an inventor or a firm have been inventing, $K$ would be the number of patents, inventors or firms that have been patenting in a given class and $N$ would be the total number of occurrences (i.e. of links) in a bipartite network of patents*classes, inventors*classes or firms*classes. However, the approximation would perform very poorly in this case. 

The source of the analytic approximation deviating from the real behavior is due to the binary nature of citation networks and bipartite occurrence networks. When one works with weighted networks, numeric solutions provided by randomization algorithms that preserve row and column sums of the adjacency matrix, and analytic solutions based on hypergeometric distributions fully agree. In contrast, with binary networks, analytic solutions based on hypergeometric random variables may considerably differ from numeric solutions. The source of the problem lays in the possibility of double counting associations. Suppose that we are measuring the expected number of citations between patents. Suppose also that patent $A$ cites patents $B$ and $C$ and that patent $D$ cites patents $B$ and $E$, and that we want to swap citations between patents. If we use a permutation algorithm (which are a popular choice for randomizing weighted networks) to randomize citations, we might incur in double counting. In our example, if we permute cited patents we might end up in a configuration in which patent $A$ cites patent $B$ twice and patent $D$ cites patents $C$ and $E$. This would obviously break the binary nature of patent citations networks, and make the model deviate from reality. If our example would now describe a fictitious weighted networks we would not be facing double-counting problems as the random realization of the network would just strengthen the link between node $A$ and node $B$. Algorithms like BiRewire randomize networks while automatically preserving row and column sums of the empirical adjacency matrix, but also avoiding the possibility of double counting associations (this is accomplished by repeatedly swapping associations within sub-matrices of four cells in which associations are only found in one of the two diagonals). Analytic approaches based on hypergeometric distributions provide the exact solutions for permutation algorithms and are blind to the possibility of double-counting. Therefore, if we use them to predict expected values and standard deviation of associations between nodes in binary networks, we incur a mistake.

\begin{figure*}[htbp]
\centering
\includegraphics[width=\textwidth]{figs/Comparison_ANAvsNUM.png} 
\caption{\textbf{Comparison of analytic and numeric solutions of the expected value (first column), standard deviation (second column) and z-scores (third column) of direct citations (first row), co-classification (second row), co-occurrences of classes in inventors' patenting histories (third row) and in firms histories (fourth row).} One data point per class pair. If the analytic and numeric solutions would agree, all data points would lay on the black solid lines. This only happens for direct citations. Red shaded lines in the z-scores panel highlights values of the z-scores equal to 2 and -2, i.e. a possible threshold of statistical significance for proximity and distance based on a normal approximation. Inference based on the analytic solutions would cause both type I and II errors for co-classification and co-occurrences. 
}\label{Comparison_ANAvsNUM}
\end{figure*}

When we deal with a large patent citation network, in which the in-degree distribution is extremely skewed (i.e. most of the nodes have a very low $K$), most of the patents cite several patents (i.e. $n$ is relatively large), and there are many links (i.e. $N$ is very large), the probability of selecting two citations to swap that will cause double-counting is very low. To understand this, suppose now that patent $A$ cites patents $B$ and $C$ (and therefore has $n=2$), that patent $B$ has been cited $K$ times and that there are $N$ citations in the network. We would face double counting of citations from patent $A$ to $B$ only if, during the randomization process, we would randomly pick the citations from $A$ to $C$ and swap it with another one directed to $B$. This will happen with probability $(1/n)*(K-1)/(N-n)$, which is very small. For these reasons, the hypergeometric-based analytic solution, designed to predict permutation algorithms, works very well in our case, even if we randomize our binary citation network by using a 2-by-2 sub-matrix diagonal swapping algorithm (Fig. \ref{Comparison_ANAvsNUM}), top row). However, for co-occurrence data, the situation is very different. 

The occurrence networks of patents-classes, inventors-classes and firms-classes have much fewer links than a patent citation network (i.e. $N$ is much smaller) and the occurrence of some classes is much more common that the appearance of citations to a given patent (i.e. $K$ is much larger compared to $N$ in our occurrence networks). Therefore, the probability of incurring in double counting, if we would use permutation algorithms, is much larger. Accordingly, 2-by-2 sub-matrix diagonal swapping algorithms, like BiRewire, must be used in this case and the misuse of hypergeometric-based analytic solutions to predict their outcome actually causes type I and II errors in the inference based on z-scores (Fig. \ref{Comparison_ANAvsNUM}, right column). For this reason, precise analytic solutions of the expected value and variance of occurrences of classes in patents, inventors' and firms' histories do not exist. We must therefore solely rely on the numeric solutions provided by our randomization method, to calculate reliable z-scores of technology proximity.        

\clearpage

\begin{figure}[p!]
\centering
\includegraphics[width=\columnwidth]{figs/Network_Correlations_Linear_2006_IPC.pdf} 
\caption{\textbf{The different measures of technology proximity, as calculated using the IPC3 classification system with patents from 1976-2006 for all measures.}}\label{Network_Correlations_Linear_2006_IPC}
\end{figure}

\clearpage

\begin{figure}[]
\centering
\includegraphics[width=\columnwidth]{figs/Impinging_Factors_USPC.png} 
\caption{\textbf{The impinging factors affecting proximity measures, calculated using the USPC classification system.}}
\end{figure}

\begin{figure}[p!]
\centering
\includegraphics[width=\columnwidth]{figs/Related_Unrelated_Percentages_Two_Column_USPC.pdf} 
\caption{\textbf{Using the USPC classification system, all measures of technology proximity showed a sparse network after normalization.}
}\label{related_unrelated_percentage_USPC}
\end{figure}

\begin{figure}[]
\centering
\includegraphics[width=\columnwidth]{figs/Impinging_Factors_IPC4.png} 
\caption{\textbf{The impinging factors affecting proximity measures, calculated using the IPC4 classification system.}}
\end{figure}

\begin{figure}[p!]
\centering
\includegraphics[width=\columnwidth]{figs/Related_Unrelated_Percentages_Two_Column_IPC4.pdf} 
\caption{\textbf{Using the IPC4 classification system, all measures of technology proximity showed a sparse network after normalization.}}\label{related_unrelated_percentage_IPC4}
\end{figure}

\clearpage

\begin{figure}[p!]
\centering
\includegraphics[width=\columnwidth]{figs/Network_Correlations_Linear_USPC.pdf} 
\caption{\textbf{The different measures of technology proximity, as calculated using the USPC classification system, had heterogeneous correlations before normalization. After normalization, however, all measures correlated.}}\label{Network_Correlations_Linear_USPC}
\end{figure}

\begin{figure}[p!]
\centering
\includegraphics[width=\columnwidth]{figs/Relatedness_Behavior_Correlation_USPC.pdf} 
\caption{\textbf{As calculated using the USPC classification system, the normalized measures of technology proximity strongly correlated with the behavior of inventors, and modestly with the behavior of firms.}}
\end{figure}

\begin{figure}[p!]
\centering
\includegraphics[width=\columnwidth]{figs/Network_Correlations_Linear_IPC4.pdf} 
\caption{\textbf{The different measures of technology proximity, as calculated using the IPC4 classification system, had heterogeneous correlations before normalization. After normalization, however, all measures correlated.}}\label{Network_Correlations_Linear_IPC4}
\end{figure}

\begin{figure}[p!]
\centering
\includegraphics[width=\columnwidth]{figs/Relatedness_Behavior_Correlation_IPC4.pdf} 
\caption{\textbf{As calculated using the IPC4 classification system, the normalized measures of technology proximity strongly correlated with the behavior of inventors, and modestly with the behavior of firms.}}
\end{figure}


\clearpage
\printbibliography

\newpage
\beginsupplement
\pagebreak
\clearpage

\begin{center}
\textbf{\large Supporting Information}
\end{center}
\section{Z-Score Inflation and Deflation of Different Proximity Measures}
All figures as Fig. \ref{z-score_deflation}. Note that cosine-based measures have different dynamics with how randomized controls' means and standard deviations alter with the number of patents in a pair of classes. However, since the mean and the standard deviation still change at different rates, their ratio still changes, which still leads to the change in the space of possible z-scores. Thus, correcting for the change in the space of possible z-scores is still necessary.

\begin{figure*}[p]
\begin{center}
\includegraphics[width=\textwidth]{figs/Z_Score_Inflation_Co-Citation_IPC.png} 
\end{center}
\caption{}
\end{figure*}

\begin{figure*}[]
\begin{center}
\includegraphics[width=\textwidth]{figs/Z_Score_Inflation_Cosine_Sim_Inputs_IPC.png} 
\end{center}
\caption{}
\end{figure*}


\begin{figure*}[]
\begin{center}
\includegraphics[width=\textwidth]{figs/Z_Score_Inflation_Cosine_Sim_Outputs_IPC.png} 
\end{center}
\caption{}
\end{figure*}

\begin{figure*}[]
\begin{center}
\includegraphics[width=\textwidth]{figs/Z_Score_Inflation_Cosine_Sim_Inputs_High_Res_IPC.png} 
\end{center}
\caption{}
\end{figure*}

\begin{figure*}[]
\begin{center}
\includegraphics[width=\textwidth]{figs/Z_Score_Inflation_Cosine_Sim_Outputs_High_Res_IPC.png} 
\end{center}
\caption{}
\end{figure*}

\begin{figure*}[]
\begin{center}
\includegraphics[width=\textwidth]{figs/Z_Score_Inflation_Co-Classification_IPC.png} 
\end{center}
\caption{}
\end{figure*}

\begin{figure*}[]
\begin{center}
\includegraphics[width=\textwidth]{figs/Z_Score_Inflation_Co-Occurrence_Inventors_Patents_IPC.png} 
\end{center}
\caption{}
\end{figure*}

\begin{figure*}[]
\begin{center}
\includegraphics[width=\textwidth]{figs/Z_Score_Inflation_Co-Occurrence_Firms_Patents_IPC.png} 
\end{center}
\caption{}
\end{figure*}

\end{document}
Normalized_Networks.bbl
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\entry{Kauffman2000}{article}{}
  \name{author}{3}{}{%
    {{}%
     {Kauffman}{K.}%
     {Stuart}{S.}%
     {}{}%
     {}{}}%
    {{}%
     {Lobo}{L.}%
     {José}{J.}%
     {}{}%
     {}{}}%
    {{}%
     {Macready}{M.}%
     {William~G.}{W.~G.}%
     {}{}%
     {}{}}%
  }
  \keyw{Combinatorial optimization,Optimal search,Production recipes,Search
  distance,Technology landscape}
  \strng{namehash}{KSLJMWG1}
  \strng{fullhash}{KSLJMWG1}
  \field{abstract}{%
  We address the question of how a firm’s current location in the space of
  technological possibilities constrain its search for technological
  improvements. We formalize a quantitative notion of distance between
  technologies — encompassing the distinction between evolutionary changes
  (small distance) versus revolutionary change (large distance) — and
  introduce a technology landscape into an otherwise standard dynamic
  programming setting where the optimal strategy is to assign a reservation
  price to each possible technology. Technological search is modeled as
  movement, constrained by the cost of search, on a technology landscape.
  Simulations are presented on a stylized technology landscape while analytic
  results are derived using landscapes that are similar to Markov random
  fields. We find that early in the search for technological improvements, if
  the initial position is poor or average, it is optimal to search far away on
  the technology landscape; but as the firm succeeds in finding technological
  improvements it is optimal to confine search to a local region of the
  landscape.%
  }
  \verb{doi}
  \verb 10.1016/S0167-2681(00)00114-1
  \endverb
  \field{issn}{0167-2681}
  \field{number}{2}
  \field{pages}{141\bibrangedash 166}
  \field{shortjournal}{Journal of Economic Behavior \& Organization}
  \field{title}{Optimal search on a technology landscape}
  \verb{url}
  \verb http://www.sciencedirect.com/science/article/pii/S0167268100001141
  \endverb
  \field{volume}{43}
  \verb{file}
  \verb ScienceDirect Snapshot:/Users/jeff/Library/Application Support/Zotero/P
  \verb rofiles/eaysq4eo.default/zotero/storage/8AFJ6BZD/S0167268100001141.html
  \verb :
  \endverb
  \field{journaltitle}{Journal of Economic Behavior \& Organization}
  \field{month}{10}
  \field{year}{2000}
  \field{urlday}{18}
  \field{urlmonth}{05}
  \field{urlyear}{2015}
\endentry

\entry{Strumsky2002}{report}{}
  \name{author}{2}{}{%
    {{}%
     {Strumsky}{S.}%
     {Deborah}{D.}%
     {}{}%
     {}{}}%
    {{}%
     {Lobo}{L.}%
     {José}{J.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{SDLJ1}
  \strng{fullhash}{SDLJ1}
  \field{shorttitle}{“{{If}} it {{Isn}}’t {{Broken}}, {{Don}}’t {{Fix}}
  it}
  \field{title}{“{{If}} it {{Isn}}’t {{Broken}}, {{Don}}’t {{Fix}} it:”
  {{Extremal Search}} on a {{Technology Landscape}}}
  \verb{url}
  \verb http://www.santafe.edu/media/workingpapers/03-02-003.pdf
  \endverb
  \list{institution}{1}{%
    {{Working Paper 03-02-003 (Santa Fe Institute, 2002)}}%
  }
  \verb{file}
  \verb [PDF] from santafe.edu:/Users/jeff/Library/Application Support/Zotero/P
  \verb rofiles/eaysq4eo.default/zotero/storage/PRXMT2AE/Strumsky and Lobo - 20
  \verb 02 - “If it Isn’t Broken, Don’t Fix it” Extremal Searc.pdf:appl
  \verb ication/pdf
  \endverb
  \field{year}{2002}
  \field{urlday}{18}
  \field{urlmonth}{05}
  \field{urlyear}{2015}
\endentry

\entry{Fleming2004}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Fleming}{F.}%
     {Lee}{L.}%
     {}{}%
     {}{}}%
    {{}%
     {Sorenson}{S.}%
     {Olav}{O.}%
     {}{}%
     {}{}}%
  }
  \keyw{cou-,coupling,invention,Invention,recombinant search,recombinant
  search,Science,science}
  \strng{namehash}{FLSO1}
  \strng{fullhash}{FLSO1}
  \field{abstract}{%
  A large body of work argues that scientific research increases the rate of
  technological advance, and with it economic growth. The precise mechanism
  through which science accelerates the rate of invention, however, remains an
  open question. Conceptualizing invention as a combinatorial search process,
  this paper argues that science alters inventors' search processes, by leading
  them more directly to useful combinations, eliminating fruitless paths of
  research, and motivating them to continue even in the face of negative
  feedback. These mechanisms prove most useful when inventors attempt to
  combine highly coupled components; therefore, the value of scientific
  research to invention varies systematically across applications. Empirical
  analyses of patent data support this thesis. Copyright © 2004 John Wiley \&
  Sons, Ltd.%
  }
  \verb{doi}
  \verb 10.1002/smj.384
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  \field{number}{8-9}
  \field{pages}{909\bibrangedash 928}
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  \field{title}{Science as a map in technological search}
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  \verb http://onlinelibrary.wiley.com/doi/10.1002/smj.384/abstract
  \endverb
  \field{volume}{25}
  \field{langid}{english}
  \verb{file}
  \verb Fleming_Sorenson_2004.pdf:/Users/jeff/Library/Application Support/Zoter
  \verb o/Profiles/eaysq4eo.default/zotero/storage/9C7A9DAB/Fleming_Sorenson_20
  \verb 04.pdf:application/pdf;Snapshot:/Users/jeff/Library/Application Support
  \verb /Zotero/Profiles/eaysq4eo.default/zotero/storage/IWGHP2H8/abstract.html
  \verb :
  \endverb
  \field{journaltitle}{Strategic Management Journal}
  \field{day}{01}
  \field{month}{08}
  \field{year}{2004}
  \field{urlday}{18}
  \field{urlmonth}{05}
  \field{urlyear}{2015}
\endentry

\entry{Silverberg2005}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Silverberg}{S.}%
     {Gerald}{G.}%
     {}{}%
     {}{}}%
    {{}%
     {Verspagen}{V.}%
     {Bart}{B.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{SGVB1}
  \strng{fullhash}{SGVB1}
  \verb{doi}
  \verb 10.1016/j.jedc.2003.05.005
  \endverb
  \field{issn}{01651889}
  \field{number}{1-2}
  \field{pages}{225\bibrangedash 244}
  \field{title}{A percolation model of innovation in complex technology spaces}
  \verb{url}
  \verb http://linkinghub.elsevier.com/retrieve/pii/S0165188904000132
  \endverb
  \field{volume}{29}
  \field{langid}{english}
  \verb{file}
  \verb Silverberg & Verspagen, 2005.pdf:/Users/jeff/Library/Application Suppor
  \verb t/Zotero/Profiles/eaysq4eo.default/zotero/storage/I6KKCEJS/Silverberg &
  \verb  Verspagen, 2005.pdf:application/pdf
  \endverb
  \field{journaltitle}{Journal of Economic Dynamics and Control}
  \field{month}{01}
  \field{year}{2005}
  \field{urlday}{18}
  \field{urlmonth}{10}
  \field{urlyear}{2014}
\endentry

\entry{Frenken2006}{article}{}
  \name{author}{1}{}{%
    {{}%
     {Frenken}{F.}%
     {Koen}{K.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{FK1}
  \strng{fullhash}{FK1}
  \verb{doi}
  \verb 10.1016/j.strueco.2006.01.001
  \endverb
  \field{issn}{0954349X}
  \field{number}{3}
  \field{pages}{288\bibrangedash 305}
  \field{title}{A fitness landscape approach to technological complexity,
  modularity, and vertical disintegration}
  \verb{url}
  \verb http://linkinghub.elsevier.com/retrieve/pii/S0954349X0600004X
  \endverb
  \field{volume}{17}
  \field{langid}{english}
  \verb{file}
  \verb Frenken, 2006.pdf:/Users/jeff/Library/Application Support/Zotero/Profil
  \verb es/eaysq4eo.default/zotero/storage/5C93ZFTC/Frenken, 2006.pdf:applicati
  \verb on/pdf
  \endverb
  \field{journaltitle}{Structural Change and Economic Dynamics}
  \field{month}{09}
  \field{year}{2006}
  \field{urlday}{18}
  \field{urlmonth}{10}
  \field{urlyear}{2014}
\endentry

\entry{Silverberg2007}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Silverberg}{S.}%
     {Gerald}{G.}%
     {}{}%
     {}{}}%
    {{}%
     {Verspagen}{V.}%
     {Bart}{B.}%
     {}{}%
     {}{}}%
  }
  \keyw{C15,C63,Clustering,Computer Appl. in Social and Behavioral
  Sciences,D83,Economic Theory,Finance /Banking,Game Theory/Mathematical
  Methods,Innovation,Mathematical and Computational
  Physics,O31,Percolation,R&D,Search,Self-organized criticality,Technological
  change}
  \strng{namehash}{SGVB1}
  \strng{fullhash}{SGVB1}
  \field{abstract}{%
  We extend an earlier model of innovation dynamics based on percolation by
  adding endogenous R\&D search by economically motivated firms. The \{0, 1\}
  seeding of the technology lattice is now replaced by draws from a lognormal
  distribution for technology ‘difficulty’. Firms are rewarded for
  successful innovations by increases in their R\&D budget. We compare two
  regimes. In the first, firms are fixed in a region of technology space. In
  the second, they can change their location by myopically comparing progress
  in their local neighborhoods and probabilistically moving to the region with
  the highest recent progress. We call this the moving or self-organizational
  regime (SO). The SO regime always outperforms the fixed one, but its
  performance is a complex function of the ‘rationality’ of firm search (in
  terms of search radius and speed of movement). The clustering of firms in the
  SO regime grows rapidly and then fluctuates in a complex way around a high
  value that increases with the search radius. We also investigate the size
  distributions of the innovations generated in each regime. In the fixed one,
  the distribution is approximately lognormal and certainly not fat tailed. In
  the SO regime, the distributions are radically different. They are much more
  highly right skewed and show scaling over at least 2 decades with a slope
  around one, for a wide range of parameter settings. Thus we argue that firm
  self-organization leads to self-organized criticality.%
  }
  \verb{doi}
  \verb 10.1007/s11403-007-0023-1
  \endverb
  \field{issn}{1860-711X, 1860-7128}
  \field{number}{2}
  \field{pages}{195\bibrangedash 210}
  \field{shortjournal}{J Econ Interac Coord}
  \field{title}{Self-organization of {{R}}\&D search in complex technology
  spaces}
  \verb{url}
  \verb http://link.springer.com/article/10.1007/s11403-007-0023-1
  \endverb
  \field{volume}{2}
  \field{langid}{english}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/5TCPGGBG/10.html:;Full Text PDF:/Users/jeff/L
  \verb ibrary/Application Support/Zotero/Profiles/eaysq4eo.default/zotero/stor
  \verb age/FBNJX9XC/Silverberg and Verspagen - 2007 - Self-organization of R&D
  \verb  search in complex technol.pdf:application/pdf
  \endverb
  \field{journaltitle}{Journal of Economic Interaction and Coordination}
  \field{day}{01}
  \field{month}{12}
  \field{year}{2007}
  \field{urlday}{18}
  \field{urlmonth}{10}
  \field{urlyear}{2014}
\endentry

\entry{Kay2014}{article}{}
  \name{author}{4}{}{%
    {{}%
     {Kay}{K.}%
     {Luciano}{L.}%
     {}{}%
     {}{}}%
    {{}%
     {Newman}{N.}%
     {Nils}{N.}%
     {}{}%
     {}{}}%
    {{}%
     {Youtie}{Y.}%
     {Jan}{J.}%
     {}{}%
     {}{}}%
    {{}%
     {Porter}{P.}%
     {Alan~L}{A.~L.}%
     {}{}%
     {}{}}%
  }
  \keyw{Jeff_Read,Technology_Development}
  \strng{namehash}{KL+1}
  \strng{fullhash}{KLNNYJPAL1}
  \verb{doi}
  \verb 10.1002/asi
  \endverb
  \field{number}{12}
  \field{pages}{2432\bibrangedash 2443}
  \field{title}{Patent {{Overlay Mapping}} : {{Visualizing Technological
  Distance}}}
  \field{volume}{65}
  \verb{file}
  \verb Kay_et_al_2014.pdf:/Users/jeff/Library/Application Support/Zotero/Profi
  \verb les/eaysq4eo.default/zotero/storage/DVR4ICP5/Kay_et_al_2014.pdf:applica
  \verb tion/pdf
  \endverb
  \field{journaltitle}{Journal of the Association for Information Science and
  Technology}
  \field{year}{2014}
\endentry

\entry{Leydesdorff2014}{article}{}
  \name{author}{3}{}{%
    {{}%
     {Leydesdorff}{L.}%
     {Loet}{L.}%
     {}{}%
     {}{}}%
    {{}%
     {Kushnir}{K.}%
     {Duncan}{D.}%
     {}{}%
     {}{}}%
    {{}%
     {Rafols}{R.}%
     {Ismael}{I.}%
     {}{}%
     {}{}}%
  }
  \keyw{amsterdam school of communication,ascor,classification á overlay,ipc
  á patent á,l,leydesdorff,map á uspto á,Research,university of amsterdam}
  \strng{namehash}{LLKDRI1}
  \strng{fullhash}{LLKDRI1}
  \verb{doi}
  \verb 10.1007/s11192-012-0923-2
  \endverb
  \field{issn}{1119201209}
  \field{number}{3}
  \field{pages}{1583\bibrangedash 1599}
  \field{title}{Interactive overlay maps for {{US}} patent ({{USPTO}}) data
  based on {{International Patent Classification}} ({{IPC}})}
  \verb{url}
  \verb http://link.springer.com/10.1007/s11192-012-0923-2
  \endverb
  \field{volume}{98}
  \verb{file}
  \verb Leydesdorff_Kushnir_Rafols_2012.pdf:/Users/jeff/Library/Application Sup
  \verb port/Zotero/Profiles/eaysq4eo.default/zotero/storage/W8HSSGUG/Leydesdor
  \verb ff_Kushnir_Rafols_2012.pdf:application/pdf
  \endverb
  \field{journaltitle}{Scientometrics}
  \field{year}{2014}
\endentry

\entry{Breschi2003}{article}{}
  \name{author}{3}{}{%
    {{}%
     {Breschi}{B.}%
     {Stefano}{S.}%
     {}{}%
     {}{}}%
    {{}%
     {Lissoni}{L.}%
     {Francesco}{F.}%
     {}{}%
     {}{}}%
    {{}%
     {Malerba}{M.}%
     {Franco}{F.}%
     {}{}%
     {}{}}%
  }
  \keyw{Co-classification,Diversification,Patents,Relatedness}
  \strng{namehash}{BSLFMF1}
  \strng{fullhash}{BSLFMF1}
  \field{abstract}{%
  This paper claims that knowledge-relatedness is a key factor in affecting
  firms’ technological diversification. The hypothesis is tested that firms
  extend the range of their innovative activities in a non-random way.
  Specifically, we test the extent to which firms diversify their innovative
  activities across related technological fields, i.e. fields that share a
  common knowledge base and rely upon common heuristics and scientific
  principles. The paper proposes an original measure of knowledge-relatedness,
  using co-classification codes contained in patent documents, and examines the
  patterns of technological diversification of the whole population of firms
  from the United States, Italy, France, UK, Germany, and Japan patenting to
  the European Patent Office from 1982 to 1993. Robust evidence is found that
  knowledge-relatedness is a major feature of firms’ innovative activities.%
  }
  \verb{doi}
  \verb 10.1016/S0048-7333(02)00004-5
  \endverb
  \field{issn}{0048-7333}
  \field{number}{1}
  \field{pages}{69\bibrangedash 87}
  \field{shortjournal}{Research Policy}
  \field{title}{Knowledge-relatedness in firm technological diversification}
  \verb{url}
  \verb http://www.sciencedirect.com/science/article/pii/S0048733302000045
  \endverb
  \field{volume}{32}
  \verb{file}
  \verb ScienceDirect Full Text PDF:/Users/jeff/Library/Application Support/Zot
  \verb ero/Profiles/eaysq4eo.default/zotero/storage/4QWX9NQC/Breschi et al. -
  \verb 2003 - Knowledge-relatedness in firm technological divers.pdf:applicati
  \verb on/pdf;Breschi et al., 2003.pdf:/Users/jeff/Library/Application Support
  \verb /Zotero/Profiles/eaysq4eo.default/zotero/storage/FKA6SRU5/Breschi et al
  \verb ., 2003.pdf:application/pdf;ScienceDirect Snapshot:/Users/jeff/Library/
  \verb Application Support/Zotero/Profiles/eaysq4eo.default/zotero/storage/RGT
  \verb RZF4P/S0048733302000045.html:
  \endverb
  \field{journaltitle}{Research Policy}
  \field{month}{01}
  \field{year}{2003}
  \field{urlday}{26}
  \field{urlmonth}{09}
  \field{urlyear}{2014}
\endentry

\entry{Arthur2009}{book}{}
  \name{author}{1}{}{%
    {{}%
     {Arthur}{A.}%
     {W.~Brian}{W.~B.}%
     {}{}%
     {}{}}%
  }
  \list{publisher}{1}{%
    {{Simon and Schuster}}%
  }
  \keyw{Jeff_Read}
  \strng{namehash}{AWB1}
  \strng{fullhash}{AWB1}
  \field{abstract}{%
  “More than anything else technology creates our world. It creates our
  wealth, our economy, our very way of being,” says W. Brian Arthur. Yet
  despite technology’s irrefutable importance in our daily lives, until now
  its major questions have gone unanswered. Where do new technologies come
  from? What constitutes innovation, and how is it achieved? Does technology,
  like biological life, evolve? In this groundbreaking work, pioneering
  technology thinker and economist W. Brian Arthur answers these questions and
  more, setting forth a boldly original way of thinking about technology. The
  Nature of Technology is an elegant and powerful theory of technology’s
  origins and evolution. Achieving for the development of technology what
  Thomas Kuhn’s The Structure of Scientific Revolutions did for scientific
  progress, Arthur explains how transformative new technologies arise and how
  innovation really works. Drawing on a wealth of examples, from historical
  inventions to the high-tech wonders of today, Arthur takes us on a
  mind-opening journey that will change the way we think about technology and
  how it structures our lives. The Nature of Technology is a classic for our
  times.%
  }
  \field{isbn}{1-4391-6578-5}
  \field{pagetotal}{256}
  \field{title}{The {{Nature}} of {{Technology}}: {{What It Is}} and {{How It
  Evolves}}}
  \verb{file}
  \verb Arthur_2009.epub:/Users/jeff/Library/Application Support/Zotero/Profile
  \verb s/eaysq4eo.default/zotero/storage/NBVJ8GGX/Arthur - 2009 - The Nature o
  \verb f Technology What It Is and How It Ev.epub:;Arthur_2009.txt:/Users/jeff
  \verb /Library/Application Support/Zotero/Profiles/eaysq4eo.default/zotero/st
  \verb orage/STJ59U6Q/Arthur_2009.txt:;Arthur_2009.pdf:/Users/jeff/Library/App
  \verb lication Support/Zotero/Profiles/eaysq4eo.default/zotero/storage/X2KQ9N
  \verb 3M/The Nature of Technology_ What It Is and H - W. Brian Arthur.pdf:app
  \verb lication/pdf
  \endverb
  \field{year}{2009}
\endentry

\entry{Shiffrin2004}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Shiffrin}{S.}%
     {Richard~M.}{R.~M.}%
     {}{}%
     {}{}}%
    {{}%
     {Börner}{B.}%
     {Katy}{K.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{SRMBK1}
  \strng{fullhash}{SRMBK1}
  \verb{doi}
  \verb 10.1073/pnas.0307852100
  \endverb
  \verb{eprint}
  \verb 14742869
  \endverb
  \field{issn}{0027-8424, 1091-6490}
  \field{issue}{suppl 1}
  \field{pages}{5183\bibrangedash 5185}
  \field{shortjournal}{PNAS}
  \field{title}{Mapping knowledge domains}
  \verb{url}
  \verb http://www.pnas.org/content/101/suppl_1/5183
  \endverb
  \field{volume}{101}
  \field{langid}{english}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/3CWJF8NZ/5183.html:;Full Text PDF:/Users/jeff
  \verb /Library/Application Support/Zotero/Profiles/eaysq4eo.default/zotero/st
  \verb orage/5GEQE9NW/Shiffrin and Börner - 2004 - Mapping knowledge domains.
  \verb pdf:application/pdf
  \endverb
  \field{journaltitle}{Proceedings of the National Academy of Sciences}
  \field{eprinttype}{pmid}
  \field{day}{04}
  \field{month}{06}
  \field{year}{2004}
  \field{urlday}{18}
  \field{urlmonth}{05}
  \field{urlyear}{2015}
\endentry

\entry{Mane2004}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Mane}{M.}%
     {Ketan~K.}{K.~K.}%
     {}{}%
     {}{}}%
    {{}%
     {Börner}{B.}%
     {Katy}{K.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{MKKBK1}
  \strng{fullhash}{MKKBK1}
  \field{abstract}{%
  Scientific research is highly dynamic. New areas of science continually
  evolve; others gain or lose importance, merge, or split. Due to the steady
  increase in the number of scientific publications, it is hard to keep an
  overview of the structure and dynamic development of one's own field of
  science, much less all scientific domains. However, knowledge of “hot”
  topics, emergent research frontiers, or change of focus in certain areas is a
  critical component of resource allocation decisions in research laboratories,
  governmental institutions, and corporations. This paper demonstrates the
  utilization of Kleinberg's burst detection algorithm, co-word occurrence
  analysis, and graph layout techniques to generate maps that support the
  identification of major research topics and trends. The approach was applied
  to analyze and map the complete set of papers published in PNAS in the years
  1982-2001. Six domain experts examined and commented on the resulting maps in
  an attempt to reconstruct the evolution of major research areas covered by
  PNAS.%
  }
  \verb{doi}
  \verb 10.1073/pnas.0307626100
  \endverb
  \verb{eprint}
  \verb 14978278
  \endverb
  \field{issn}{0027-8424, 1091-6490}
  \field{issue}{suppl 1}
  \field{pages}{5287\bibrangedash 5290}
  \field{shortjournal}{PNAS}
  \field{title}{Mapping topics and topic bursts in {{PNAS}}}
  \verb{url}
  \verb http://www.pnas.org/content/101/suppl_1/5287
  \endverb
  \field{volume}{101}
  \field{langid}{english}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/33NU6K7M/5287.html:;Mane_Börner_2004.pdf:/Us
  \verb ers/jeff/Library/Application Support/Zotero/Profiles/eaysq4eo.default/z
  \verb otero/storage/9HU5PGR2/Mane_Börner_2004.pdf:application/pdf
  \endverb
  \field{journaltitle}{Proceedings of the National Academy of Sciences}
  \field{eprinttype}{pmid}
  \field{day}{04}
  \field{month}{06}
  \field{year}{2004}
  \field{urlday}{31}
  \field{urlmonth}{07}
  \field{urlyear}{2015}
\endentry

\entry{Verspagen1997a}{article}{}
  \name{author}{1}{}{%
    {{}%
     {Verspagen}{V.}%
     {Bart}{B.}%
     {}{}%
     {}{}}%
  }
  \keyw{Economics general,International Economics,O30,O47}
  \strng{namehash}{VB1}
  \strng{fullhash}{VB1}
  \field{abstract}{%
  Estimating International Technology Spillovers Using Technology Flow
  Matrices. — This paper investigates the impact of international R \& D
  spillovers on sectoral growth patterns in OECD countries. It applies panel
  regression techniques to a time-series cross-section panel. It arrives at the
  conclusion that knowledge spillovers are an important contributor to economic
  growth. The estimation results are applied in the form of a ‘simulation’
  of TFP growth per country, splitting (R \& D-related) TFP into a component
  due to domestic R \& D and one due to foreign R \& D. The results also show
  that the United States and Germany are the most influential countries in
  terms of contributions to other countries’ TFP growth.%
  }
  \verb{doi}
  \verb 10.1007/BF02707461
  \endverb
  \field{issn}{0043-2636, 1610-2886}
  \field{number}{2}
  \field{pages}{226\bibrangedash 248}
  \field{shortjournal}{Weltwirtschaftliches Archiv}
  \field{title}{Estimating international technology spillovers using technology
  flow matrices}
  \verb{url}
  \verb http://link.springer.com/article/10.1007/BF02707461
  \endverb
  \field{volume}{133}
  \field{langid}{english}
  \field{journaltitle}{Weltwirtschaftliches Archiv}
  \field{month}{06}
  \field{year}{1997}
  \field{urlday}{16}
  \field{urlmonth}{04}
  \field{urlyear}{2016}
\endentry

\entry{Jaffe1986}{report}{}
  \name{author}{1}{}{%
    {{}%
     {Jaffe}{J.}%
     {Adam~B.}{A.~B.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{JAB1}
  \strng{fullhash}{JAB1}
  \field{abstract}{%
  This paper presents evidence that firms' patents, profits and market value
  are systematically related to the"technological position" of firms' research
  programs. Further, firms are seen to "move" in technology space in response
  to the pattern of contemporaneous profits at different positions. These
  movements tend to erode excess returns."Spillovers" of R\&D are modelled by
  examining whether the R\&D of neighboring firms in technology space has an
  observable impact on the firm's R\&D success. Firms whose neighbors do much
  R\&D produce more patents per dollar of their own R\&D,with a positive
  interaction that gives high R\&D firms the largest benefit from spillovers.
  In terms of profit and market value, however, their are both positive and
  negative effects of nearby firms' R\&D. The net effect is positive for high
  R\&D firms, but firms with R\&D about one standard deviation below the mean
  are made worse off overall by the R\&D of others.%
  }
  \field{number}{1815}
  \field{shorttitle}{Technological {{Opportunity}} and {{Spillovers}} of
  {{R}}\&D}
  \field{title}{Technological {{Opportunity}} and {{Spillovers}} of {{R}}\&D:
  {{Evidence}} from {{Firms}}' {{Patents}}, {{Profits}} and {{Market Value}}}
  \verb{url}
  \verb http://www.nber.org/papers/w1815
  \endverb
  \list{institution}{1}{%
    {{National Bureau of Economic Research}}%
  }
  \verb{file}
  \verb NBER Full Text PDF:/Users/jeff/Library/Application Support/Zotero/Profi
  \verb les/eaysq4eo.default/zotero/storage/9E8GEEKX/Jaffe - 1986 - Technologic
  \verb al Opportunity and Spillovers of R&D E.pdf:application/pdf
  \endverb
  \field{type}{Working {{Paper}}}
  \field{year}{1986}
  \field{urlday}{18}
  \field{urlmonth}{05}
  \field{urlyear}{2015}
\endentry

\entry{Leten2007}{article}{}
  \name{author}{3}{}{%
    {{}%
     {Leten}{L.}%
     {Bart}{B.}%
     {}{}%
     {}{}}%
    {{}%
     {Belderbos}{B.}%
     {Rene}{R.}%
     {}{}%
     {}{}}%
    {{}%
     {Van~Looy}{V.~L.}%
     {Bart}{B.}%
     {}{}%
     {}{}}%
  }
  \keyw{Bowen_To_Read}
  \strng{namehash}{LBBRVLB1}
  \strng{fullhash}{LBBRVLB1}
  \field{abstract}{%
  Technological diversification at the firm level (i.e., the expansion of a
  firm's technology base into a wide range of technology fields) is found to be
  a prevailing phenomenon in all three major industrialized regions,—the
  United States, Europe, and Japan—prompting the term multitechnology
  corporation. Whereas previous studies have provided insights into the
  composition of technology portfolios of multitechnology firms, little is
  known about the relationship between technological diversification and firms'
  technological performance. Against a backdrop of the technology and
  innovation management literature, the present article investigates the
  relationship between technological diversification and technological
  performance, taking into account the moderating role of technological
  coherence in firms' technology portfolios. Hereby, technological coherence is
  defined as the degree to which technologies in a technology portfolio are
  technologically related. To measure the technological coherence of
  portfolios, a measure of technological relatedness of technology fields is
  constructed based on patent citation patterns found in 450,000 European
  Patent Office (EPO) patent grants. Two hypotheses are presented here: (1)
  Technological diversification has an inverted U-shaped relationship with
  technological performance; and (2) technological coherence moderates the
  relationship between technological diversification and technological
  performance positively. These hypotheses are tested empirically using a panel
  data set (1995–2003) on patent portfolios pertaining to 184 U.S., European,
  and Japanese firms. The firms selected are the largest research and
  development (R\&D) actors in five industries: pharmaceuticals and
  biotechnology; chemicals; engineering and general machinery; information
  technology (IT) hardware (i.e., computers and communication equipment); and
  electronics and electrical machinery. Empirical results, obtained by
  fixed-effects negative binomial regressions, support both hypotheses in the
  present article. Technological diversification has an inverted U-shaped
  relationship with technological performance. Technological diversification
  offers opportunities for cross-fertilization and technology fusion, but high
  levels of diversification may yield few marginal benefits as firms risk
  lacking sufficient levels of scale to benefit from wide-ranging technological
  diversification, and firms may encounter high levels of coordination and
  integration costs. Further, the results show that the net benefits of
  technological diversification are higher in technologically coherent
  technology portfolios. If firms build up a technologically coherent
  diversified portfolio, the presence of sufficient levels of scale is ensured
  and coordination costs are limited. At the same time, technologically
  coherent diversification puts firms in a better position to benefit form
  cross-fertilization between technologies. The present article clearly
  identifies the important role of technological coherence in technology
  diversification strategies of firms.%
  }
  \verb{doi}
  \verb 10.1111/j.1540-5885.2007.00272.x
  \endverb
  \field{issn}{1540-5885}
  \field{number}{6}
  \field{pages}{567\bibrangedash 579}
  \field{title}{Technological {{Diversification}}, {{Coherence}}, and
  {{Performance}} of {{Firms}}}
  \verb{url}
  \verb http://onlinelibrary.wiley.com/doi/10.1111/j.1540-5885.2007.00272.x/abs
  \verb tract
  \endverb
  \field{volume}{24}
  \field{langid}{english}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/AWHGRVNW/full.html:;Full Text PDF:/Users/jeff
  \verb /Library/Application Support/Zotero/Profiles/eaysq4eo.default/zotero/st
  \verb orage/CP7UZGH4/Leten et al. - 2007 - Technological Diversification, Coh
  \verb erence, and Perf.pdf:application/pdf;Snapshot:/Users/jeff/Library/Appli
  \verb cation Support/Zotero/Profiles/eaysq4eo.default/zotero/storage/KW7IH5MD
  \verb /abstract.html:;Leten et al., 2007.pdf:/Users/jeff/Library/Application
  \verb Support/Zotero/Profiles/eaysq4eo.default/zotero/storage/SXJGHV7P/Leten
  \verb et al., 2007.pdf:application/pdf
  \endverb
  \field{journaltitle}{Journal of Product Innovation Management}
  \field{day}{01}
  \field{month}{11}
  \field{year}{2007}
  \field{urlday}{26}
  \field{urlmonth}{09}
  \field{urlyear}{2014}
\endentry

\entry{Uzzi2013}{article}{}
  \name{author}{4}{}{%
    {{}%
     {Uzzi}{U.}%
     {Brian}{B.}%
     {}{}%
     {}{}}%
    {{}%
     {Mukherjee}{M.}%
     {Satyam}{S.}%
     {}{}%
     {}{}}%
    {{}%
     {Stringer}{S.}%
     {Michael}{M.}%
     {}{}%
     {}{}}%
    {{}%
     {Jones}{J.}%
     {Ben}{B.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{UB+1}
  \strng{fullhash}{UBMSSMJB1}
  \field{abstract}{%
  Novelty is an essential feature of creative ideas, yet the building blocks of
  new ideas are often embodied in existing knowledge. From this perspective,
  balancing atypical knowledge with conventional knowledge may be critical to
  the link between innovativeness and impact. Our analysis of 17.9 million
  papers spanning all scientific fields suggests that science follows a nearly
  universal pattern: The highest-impact science is primarily grounded in
  exceptionally conventional combinations of prior work yet simultaneously
  features an intrusion of unusual combinations. Papers of this type were twice
  as likely to be highly cited works. Novel combinations of prior work are
  rare, yet teams are 37.7\% more likely than solo authors to insert novel
  combinations into familiar knowledge domains. Making an Impact How big a role
  do unconventional combinations of existing knowledge play in the impact of a
  scientific paper? To examine this question, Uzzi et al. (p. 468) studied 17.9
  million research articles across five decades of the Web of Science, the
  largest repository of scientific research. Scientific work typically appeared
  to draw on highly conventional, familiar mixtures of knowledge. The
  highest-impact papers were not the ones that had the greatest novelty, but
  had a combination of novelty and otherwise conventional combinations of prior
  work.%
  }
  \verb{doi}
  \verb 10.1126/science.1240474
  \endverb
  \verb{eprint}
  \verb 24159044
  \endverb
  \field{issn}{0036-8075, 1095-9203}
  \field{number}{6157}
  \field{pages}{468\bibrangedash 472}
  \field{shortjournal}{Science}
  \field{title}{Atypical {{Combinations}} and {{Scientific Impact}}}
  \verb{url}
  \verb http://www.sciencemag.org/content/342/6157/468
  \endverb
  \field{volume}{342}
  \field{langid}{english}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/273QB2JP/468.html:;Uzzi et al_2013.pdf:/Users
  \verb /jeff/Library/Application Support/Zotero/Profiles/eaysq4eo.default/zote
  \verb ro/storage/WXNV9VMA/Uzzi et al_2013.pdf:application/pdf
  \endverb
  \field{journaltitle}{Science}
  \field{eprinttype}{pmid}
  \field{day}{25}
  \field{month}{10}
  \field{year}{2013}
  \field{urlday}{09}
  \field{urlmonth}{09}
  \field{urlyear}{2015}
\endentry

\entry{Wallace2009}{article}{}
  \name{author}{3}{}{%
    {{}%
     {Wallace}{W.}%
     {Matthew~L.}{M.~L.}%
     {}{}%
     {}{}}%
    {{}%
     {Gingras}{G.}%
     {Yves}{Y.}%
     {}{}%
     {}{}}%
    {{}%
     {Duhon}{D.}%
     {Russell}{R.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{WMLGYDR1}
  \strng{fullhash}{WMLGYDR1}
  \field{abstract}{%
  We use a technique recently developed by V. Blondel, J.-L. Guillaume, R.
  Lambiotte, and E. Lefebvre (2008) to detect scientific specialties from
  author cocitation networks. This algorithm has distinct advantages over most
  previous methods used to obtain cocitation “clusters” since it avoids the
  use of similarity measures, relies entirely on the topology of the weighted
  network, and can be applied to relatively large networks. Most importantly,
  it requires no subjective interpretation of the cocitation data or of the
  communities found. Using two examples, we show that the resulting specialties
  are the smallest coherent “groups” of researchers (within a hierarchy of
  cluster sizes) and can thus be identified unambiguously. Furthermore, we
  confirm that these communities are indeed representative of what we know
  about the structure of a given scientific discipline and that as specialties,
  they can be accurately characterized by a few keywords (from the publication
  titles). We argue that this robust and efficient algorithm is particularly
  well-suited to cocitation networks and that the results generated can be of
  great use to researchers studying various facets of the structure and
  evolution of science.%
  }
  \verb{doi}
  \verb 10.1002/asi.20987
  \endverb
  \field{issn}{1532-2890}
  \field{number}{2}
  \field{pages}{240\bibrangedash 246}
  \field{shortjournal}{J. Am. Soc. Inf. Sci.}
  \field{title}{A new approach for detecting scientific specialties from raw
  cocitation networks}
  \verb{url}
  \verb http://onlinelibrary.wiley.com/doi/10.1002/asi.20987/abstract
  \endverb
  \field{volume}{60}
  \field{langid}{english}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/G6VI2TUK/full.html:
  \endverb
  \field{journaltitle}{Journal of the American Society for Information Science
  and Technology}
  \field{day}{01}
  \field{month}{02}
  \field{year}{2009}
  \field{urlday}{09}
  \field{urlmonth}{09}
  \field{urlyear}{2015}
\endentry

\entry{SmallHenry1973}{article}{}
  \name{author}{1}{}{%
    {{}%
     {{Small, Henry}}{S.}%
     {}{}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{S1}
  \strng{fullhash}{S1}
  \verb{doi}
  \verb 10.1002/asi.4630240406
  \endverb
  \field{number}{4}
  \field{pages}{265\bibrangedash 269}
  \field{title}{Co-citation in the scientific literature: {{A}} new measure of
  the relationship between two documents}
  \verb{url}
  \verb http://onlinelibrary.wiley.com/doi/10.1002/asi.4630240406/abstract
  \endverb
  \field{volume}{24}
  \verb{file}
  \verb Co-citation in the scientific literature\: A new measure of the relatio
  \verb nship between two documents - Small - 2007 - Journal of the American So
  \verb ciety for Information Science - Wiley Online Library:/Users/jeff/Librar
  \verb y/Application Support/Zotero/Profiles/eaysq4eo.default/zotero/storage/5
  \verb PUVSW3V/abstract.html:
  \endverb
  \field{journaltitle}{Journal of the American Society for Information Science}
  \field{year}{1973}
  \field{urlday}{08}
  \field{urlmonth}{01}
  \field{urlyear}{2016}
\endentry

\entry{Leicht2006}{article}{}
  \name{author}{3}{}{%
    {{}%
     {Leicht}{L.}%
     {E.~A.}{E.~A.}%
     {}{}%
     {}{}}%
    {{}%
     {Holme}{H.}%
     {Petter}{P.}%
     {}{}%
     {}{}}%
    {{}%
     {Newman}{N.}%
     {M.~E.~J.}{M.~E.~J.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{LEAHPNMEJ1}
  \strng{fullhash}{LEAHPNMEJ1}
  \field{abstract}{%
  We consider methods for quantifying the similarity of vertices in networks.
  We propose a measure of similarity based on the concept that two vertices are
  similar if their immediate neighbors in the network are themselves similar.
  This leads to a self-consistent matrix formulation of similarity that can be
  evaluated iteratively using only a knowledge of the adjacency matrix of the
  network. We test our similarity measure on computer-generated networks for
  which the expected results are known, and on a number of real-world
  networks.%
  }
  \verb{doi}
  \verb 10.1103/PhysRevE.73.026120
  \endverb
  \field{number}{2}
  \field{pages}{026120}
  \field{shortjournal}{Phys. Rev. E}
  \field{title}{Vertex similarity in networks}
  \verb{url}
  \verb http://link.aps.org/doi/10.1103/PhysRevE.73.026120
  \endverb
  \field{volume}{73}
  \verb{file}
  \verb APS Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/ea
  \verb ysq4eo.default/zotero/storage/WGMR8ZIN/PhysRevE.73.html:
  \endverb
  \field{journaltitle}{Physical Review E}
  \field{day}{17}
  \field{month}{02}
  \field{year}{2006}
  \field{urlday}{07}
  \field{urlmonth}{01}
  \field{urlyear}{2016}
\endentry

\entry{Jaffe1989}{article}{}
  \name{author}{1}{}{%
    {{}%
     {Jaffe}{J.}%
     {Adam~B.}{A.~B.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{JAB1}
  \strng{fullhash}{JAB1}
  \verb{doi}
  \verb 10.1016/0048-7333(89)90007-3
  \endverb
  \field{issn}{0048-7333}
  \field{number}{2}
  \field{pages}{87\bibrangedash 97}
  \field{shortjournal}{Research Policy}
  \field{title}{Characterizing the "technological position" of firms, with
  application to quantifying technological opportunity and research spillovers}
  \verb{url}
  \verb http://www.sciencedirect.com/science/article/pii/0048733389900073
  \endverb
  \field{volume}{18}
  \verb{file}
  \verb ScienceDirect Snapshot:/Users/jeff/Library/Application Support/Zotero/P
  \verb rofiles/eaysq4eo.default/zotero/storage/ZNUKJMBF/0048733389900073.html:
  \endverb
  \field{journaltitle}{Research Policy}
  \field{month}{04}
  \field{year}{1989}
  \field{urlday}{18}
  \field{urlmonth}{05}
  \field{urlyear}{2015}
\endentry

\entry{Yan2015}{report}{}
  \name{author}{2}{}{%
    {{}%
     {Yan}{Y.}%
     {Bowen}{B.}%
     {}{}%
     {}{}}%
    {{}%
     {Luo}{L.}%
     {Jianxi}{J.}%
     {}{}%
     {}{}}%
  }
  \keyw{information mapping,Innovation,Patents,technological
  distance,technology networks}
  \strng{namehash}{YBLJ1}
  \strng{fullhash}{YBLJ1}
  \field{abstract}{%
  Recent works in the information science literature have presented cases of
  using patent databases and patent classification information to construct
  network maps of technology fields, which aim to aid in competitive
  intelligence analysis and innovation decision making. Constructing such a
  patent network requires a proper measure of the distance between different
  classes of patents in the patent classification systems. Despite the
  existence of various distance measures in the literature, it is unclear how
  to consistently assess and compare them, and which ones to select for
  constructing patent technology network maps. This ambiguity has limited the
  development and applications of such technology maps. Herein, we propose to
  compare alternative distance measures and identify the superior ones by
  analysing the differences and similarities in the structural properties of
  resulting patent network maps. Using United States patent data from 1976 to
  2006 and International Patent Classification system, we compare 12
  representative distance measures, which quantify inter-field knowledge base
  proximity, field-crossing diversification likelihood or frequency of
  innovation agents, and co-occurrences of patent classes in the same patents.
  Our comparative analyses suggest the patent technology network maps based on
  normalised co-reference and inventor diversification likelihood measures are
  the best representatives.%
  }
  \field{number}{ID 2660269}
  \field{title}{Measuring {{Technological Distance}} for {{Patent Mapping}}}
  \verb{url}
  \verb http://papers.ssrn.com/abstract=2660269
  \endverb
  \list{location}{1}{%
    {{Rochester, NY}}%
  }
  \list{institution}{1}{%
    {{Social Science Research Network}}%
  }
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/DSF7UWQZ/papers.html:
  \endverb
  \field{type}{{{SSRN Scholarly Paper}}}
  \field{day}{14}
  \field{month}{09}
  \field{year}{2015}
  \field{urlday}{08}
  \field{urlmonth}{01}
  \field{urlyear}{2016}
\endentry

\entry{Jeong2015}{article}{}
  \name{author}{3}{}{%
    {{}%
     {Jeong}{J.}%
     {Seongkyoon}{S.}%
     {}{}%
     {}{}}%
    {{}%
     {Kim}{K.}%
     {Jong-Chan}{J.-C.}%
     {}{}%
     {}{}}%
    {{}%
     {Choi}{C.}%
     {Jae~Young}{J.~Y.}%
     {}{}%
     {}{}}%
  }
  \keyw{91D30,Converging technology,D85,Information Storage and
  Retrieval,Interdisciplinary,Interdisciplinary Studies,Library
  Science,Technological convergence,Technology fusion mixture}
  \strng{namehash}{JSKJCCJY1}
  \strng{fullhash}{JSKJCCJY1}
  \field{abstract}{%
  Despite the enthusiasm for technology convergence seen over the last decade
  in society and the broad consensus on its considerable impact, there is
  neither any substantive evidence that technology convergence occurs overall
  nor any objective explanation of the domains where it may be found. By using
  patents filed to the KIPO from 1996 to 2010 and demonstrating trends based on
  co-classification analysis at the entire technology domain level, we
  elucidate the extent of technology convergence in a technological innovation
  system and its change in status over time. Furthermore, our paper uses
  network analysis based on patent data to identify the occurrence of
  technology convergence in terms of its technological domains. Our findings
  are as follows: (1) the diffusion of technology convergence has been ongoing
  since the early 2000s; (2) technology convergence is evolving into a more
  complex and heterogeneous form; (3) convergent technology has a wider scope
  but requires more effort to develop than does non-convergent technology; and
  (4) evidence for the strong consistency of converged domains over time
  exists. These results support the numerous initiatives of governments and
  firms to promote technology convergence and illustrate the future form of
  technology convergence.%
  }
  \verb{doi}
  \verb 10.1007/s11192-015-1606-6
  \endverb
  \field{issn}{0138-9130, 1588-2861}
  \field{number}{3}
  \field{pages}{841\bibrangedash 871}
  \field{shortjournal}{Scientometrics}
  \field{shorttitle}{Technology convergence}
  \field{title}{Technology convergence: {{What}} developmental stage are we
  in?}
  \verb{url}
  \verb http://link.springer.com/article/10.1007/s11192-015-1606-6
  \endverb
  \field{volume}{104}
  \field{langid}{english}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/7BV38EGE/s11192-015-1606-6.html:
  \endverb
  \field{journaltitle}{Scientometrics}
  \field{day}{19}
  \field{month}{05}
  \field{year}{2015}
  \field{urlday}{30}
  \field{urlmonth}{12}
  \field{urlyear}{2015}
\endentry

\entry{Dolfsma2011}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Dolfsma}{D.}%
     {Wilfred}{W.}%
     {}{}%
     {}{}}%
    {{}%
     {Leydesdorff}{L.}%
     {Loet}{L.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{DWLL1}
  \strng{fullhash}{DWLL1}
  \field{number}{3}
  \field{pages}{311\bibrangedash 326}
  \field{shorttitle}{Innovation systems as patent networks}
  \field{title}{Innovation systems as patent networks: {{The Netherlands}},
  {{India}} and nanotech}
  \verb{url}
  \verb http://www.tandfonline.com/doi/abs/10.5172/impp.2011.13.3.311
  \endverb
  \field{volume}{13}
  \verb{file}
  \verb [PDF] from arxiv.org:/Users/jeff/Library/Application Support/Zotero/Pro
  \verb files/eaysq4eo.default/zotero/storage/38VRGWHC/Dolfsma and Leydesdorff
  \verb - 2011 - Innovation systems as patent networks The Netherl.pdf:applicat
  \verb ion/pdf;Snapshot:/Users/jeff/Library/Application Support/Zotero/Profile
  \verb s/eaysq4eo.default/zotero/storage/XP84DJUQ/impp.2011.13.3.html:
  \endverb
  \field{journaltitle}{Innovation}
  \field{year}{2011}
  \field{urlday}{30}
  \field{urlmonth}{12}
  \field{urlyear}{2015}
\endentry

\entry{Joo2010}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Joo}{J.}%
     {Si~Hyung}{S.~H.}%
     {}{}%
     {}{}}%
    {{}%
     {Kim}{K.}%
     {Yeonbae}{Y.}%
     {}{}%
     {}{}}%
  }
  \keyw{Incomplete multi-dimensional contingency table,Information Storage and
  Retrieval,Interdisciplinary Studies,IPC co-occurrence,Library
  Science,Mantel–Haenszel common odds ratio,Relatedness}
  \strng{namehash}{JSHKY1}
  \strng{fullhash}{JSHKY1}
  \field{abstract}{%
  Intensified technology convergence, increasing relatedness between
  technological fields, is a mega-trend in 21st century science and technology.
  However, scientometrics has been unsuccessful in identifying this
  techno-economic paradigm change. To address the limitations and validity
  problems of conventional measures of technology convergence, we introduce a
  multi-dimensional contingency table representation of technological field
  co-occurrence and a relatedness measure based on the Mantel–Haenszel common
  log odds ratio. We used Korean patent data to compare previous and proposed
  methods. Results show that the proposed method can increase understanding of
  the techno-economic paradigm change because it reveals significant changes in
  technological relatedness over time.%
  }
  \verb{doi}
  \verb 10.1007/s11192-009-0108-9
  \endverb
  \field{issn}{0138-9130, 1588-2861}
  \field{number}{2}
  \field{pages}{435\bibrangedash 454}
  \field{shortjournal}{Scientometrics}
  \field{title}{Measuring relatedness between technological fields}
  \verb{url}
  \verb http://link.springer.com/article/10.1007/s11192-009-0108-9
  \endverb
  \field{volume}{83}
  \field{langid}{english}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/49URDX8J/10.html:;Full Text PDF:/Users/jeff/L
  \verb ibrary/Application Support/Zotero/Profiles/eaysq4eo.default/zotero/stor
  \verb age/GIEQNSZT/Joo and Kim - 2010 - Measuring relatedness between technol
  \verb ogical fields.pdf:application/pdf
  \endverb
  \field{journaltitle}{Scientometrics}
  \field{day}{01}
  \field{month}{05}
  \field{year}{2010}
  \field{urlday}{26}
  \field{urlmonth}{09}
  \field{urlyear}{2014}
\endentry

\entry{Joo2009}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Joo}{J.}%
     {Si~Hyung}{S.~H.}%
     {}{}%
     {}{}}%
    {{}%
     {Kim}{K.}%
     {Yeonbae}{Y.}%
     {}{}%
     {}{}}%
  }
  \keyw{Incomplete multi-dimensional contingency table,Information Storage and
  Retrieval,Interdisciplinary Studies,IPC co-occurrence,Library
  Science,Mantel–Haenszel common odds ratio,Relatedness}
  \strng{namehash}{JSHKY1}
  \strng{fullhash}{JSHKY1}
  \field{abstract}{%
  Intensified technology convergence, increasing relatedness between
  technological fields, is a mega-trend in 21st century science and technology.
  However, scientometrics has been unsuccessful in identifying this
  techno-economic paradigm change. To address the limitations and validity
  problems of conventional measures of technology convergence, we introduce a
  multi-dimensional contingency table representation of technological field
  co-occurrence and a relatedness measure based on the Mantel–Haenszel common
  log odds ratio. We used Korean patent data to compare previous and proposed
  methods. Results show that the proposed method can increase understanding of
  the techno-economic paradigm change because it reveals significant changes in
  technological relatedness over time.%
  }
  \verb{doi}
  \verb 10.1007/s11192-009-0108-9
  \endverb
  \field{issn}{0138-9130, 1588-2861}
  \field{number}{2}
  \field{pages}{435\bibrangedash 454}
  \field{shortjournal}{Scientometrics}
  \field{title}{Measuring relatedness between technological fields}
  \verb{url}
  \verb http://link.springer.com/article/10.1007/s11192-009-0108-9
  \endverb
  \field{volume}{83}
  \field{langid}{english}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/AAPXHCW9/s11192-009-0108-9.html:
  \endverb
  \field{journaltitle}{Scientometrics}
  \field{day}{19}
  \field{month}{11}
  \field{year}{2009}
  \field{urlday}{30}
  \field{urlmonth}{12}
  \field{urlyear}{2015}
\endentry

\entry{Leydesdorff2008}{article}{}
  \name{author}{1}{}{%
    {{}%
     {Leydesdorff}{L.}%
     {Loet}{L.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{LL1}
  \strng{fullhash}{LL1}
  \field{number}{10}
  \field{pages}{1582\bibrangedash 1597}
  \field{title}{Patent classifications as indicators of intellectual
  organization}
  \verb{url}
  \verb http://onlinelibrary.wiley.com/doi/10.1002/asi.20814/full
  \endverb
  \field{volume}{59}
  \verb{file}
  \verb [PDF] from arxiv.org:/Users/jeff/Library/Application Support/Zotero/Pro
  \verb files/eaysq4eo.default/zotero/storage/2UBJWTSS/Leydesdorff - 2008 - Pat
  \verb ent classifications as indicators of intellectu.pdf:application/pdf;Sna
  \verb pshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4eo.
  \verb default/zotero/storage/FC4AK24V/full.html:
  \endverb
  \field{journaltitle}{Journal of the American Society for Information Science
  and Technology}
  \field{year}{2008}
  \field{urlday}{30}
  \field{urlmonth}{12}
  \field{urlyear}{2015}
\endentry

\entry{Engelsman1994}{article}{useprefix}
  \name{author}{2}{}{%
    {{}%
     {Engelsman}{E.}%
     {E.~C.}{E.~C.}%
     {}{}%
     {}{}}%
    {{}%
     {Raan}{R.}%
     {A.~F.~J.}{A.~F.~J.}%
     {van}{v.}%
     {}{}}%
  }
  \strng{namehash}{EECvRAFJ1}
  \strng{fullhash}{EECvRAFJ1}
  \field{abstract}{%
  We use bibliometric (in particular patent-based) methods and techniques to
  develop a cartography of technology. Two types of maps are presented: co-word
  maps and co-classification maps. Both types of maps have been constructed for
  the entire domain of technology (the macro-level), i.e. the ensemble of all
  fields of technology in their mutual relations. Time series clearly
  illustrates the changing relations between the major clusters of technology,
  and in particular the changing role of fields which act as a “bridge”
  between clusters, or as a (declining or emerging) centre of technological
  activities within a specific cluster. Maps visualize relations between fields
  of technology. In order to have measures of the relative strength of these
  relations, we develop the concept of affinity between fields. A special
  feature of our macro-maps concerns the role of Japan in technology. A second
  hierarchical level is the combination of several fields of technology
  (meso-level). As an example we constructed a co-word map for the emerging
  “crossroad” technology optomechatronics based on patents as well as on
  scientific publications. In this way, optomechatronics is mapped from a
  technological point of view, and from a research point of view. The third
  hierarchical level concerns one specific field of technology (micro-level).
  Co-word maps have been constructed for the technology fields coating and
  crystal growing, optical equipment, and building materials. An important
  aspect of the map is the possibility to identify centers of activity within a
  specifically defined field of technology. These centers of activity may
  indicate important innovative developments, or they may reflect important
  markets. Furthermore, we introduce the concept of “technological
  peripheries”: for a specific technology field one may identify the direct
  “surroundings”; i.e. the most strongly linked fields. Also, first
  attempts are made to map the “science and technology interface” by a
  specific combination of publication and patent data. Our general conclusion
  is that the mapping methods and techniques presented in this publication
  already offer a unique way to visualize developments in fields of technology,
  and within technology as a whole. We emphasize that our technology maps are
  intended as a support tool, and never as a replacement of experts.%
  }
  \verb{doi}
  \verb 10.1016/0048-7333(94)90024-8
  \endverb
  \field{issn}{0048-7333}
  \field{number}{1}
  \field{pages}{1\bibrangedash 26}
  \field{shortjournal}{Research Policy}
  \field{title}{A patent-based cartography of technology}
  \verb{url}
  \verb http://www.sciencedirect.com/science/article/pii/0048733394900248
  \endverb
  \field{volume}{23}
  \verb{file}
  \verb ScienceDirect Snapshot:/Users/jeff/Library/Application Support/Zotero/P
  \verb rofiles/eaysq4eo.default/zotero/storage/9QFDQJ5A/0048733394900248.html:
  \endverb
  \field{journaltitle}{Research Policy}
  \field{month}{01}
  \field{year}{1994}
  \field{urlday}{18}
  \field{urlmonth}{05}
  \field{urlyear}{2015}
\endentry

\entry{Bryce2009}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Bryce}{B.}%
     {David~J.}{D.~J.}%
     {}{}%
     {}{}}%
    {{}%
     {Winter}{W.}%
     {Sidney~G.}{S.~G.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{BDJWSG1}
  \strng{fullhash}{BDJWSG1}
  \verb{doi}
  \verb 10.1287/mnsc.1090.1040
  \endverb
  \field{issn}{0025-1909}
  \field{number}{9}
  \field{pages}{1570\bibrangedash 1585}
  \field{shortjournal}{Management Science}
  \field{title}{A {{General Interindustry Relatedness Index}}}
  \verb{url}
  \verb http://pubsonline.informs.org/doi/abs/10.1287/mnsc.1090.1040
  \endverb
  \field{volume}{55}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/U6GSW48V/mnsc.1090.html:
  \endverb
  \field{journaltitle}{Management Science}
  \field{day}{10}
  \field{month}{07}
  \field{year}{2009}
  \field{urlday}{11}
  \field{urlmonth}{07}
  \field{urlyear}{2015}
\endentry

\entry{Bottazzi2010}{report}{}
  \name{author}{2}{}{%
    {{}%
     {Bottazzi}{B.}%
     {Giulio}{G.}%
     {}{}%
     {}{}}%
    {{}%
     {Pirino}{P.}%
     {Davide}{D.}%
     {}{}%
     {}{}}%
  }
  \keyw{corporate coherence,Jeff_To_Read,null model analysis,patent
  data,Relatedness,Working Paper}
  \strng{namehash}{BGPD1}
  \strng{fullhash}{BGPD1}
  \field{number}{2010/10}
  \field{series}{LEM Working Paper Series}
  \field{title}{Measuring industry relatedness and corporate coherence}
  \verb{url}
  \verb http://www.econstor.eu/handle/10419/89309
  \endverb
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/48BNS4CQ/89309.html:;Bottazzi et al. - 2011 -
  \verb  Measuring Industry Relatedness and Corporate.pdf:/Users/jeff/Library/A
  \verb pplication Support/Zotero/Profiles/eaysq4eo.default/zotero/storage/Q5JT
  \verb 4NUZ/Bottazzi et al. - 2011 - Measuring Industry Relatedness and Corpor
  \verb ate.pdf:application/pdf;Full Text PDF:/Users/jeff/Library/Application S
  \verb upport/Zotero/Profiles/eaysq4eo.default/zotero/storage/Q8N68B3A/Bottazz
  \verb i and Pirino - 2010 - Measuring industry relatedness and corporate cohe
  \verb r.pdf:application/pdf
  \endverb
  \field{type}{Working {{Paper}}}
  \field{year}{2010}
  \field{urlday}{22}
  \field{urlmonth}{10}
  \field{urlyear}{2014}
\endentry

\entry{Teece1994}{article}{}
  \name{author}{4}{}{%
    {{}%
     {Teece}{T.}%
     {David~J.}{D.~J.}%
     {}{}%
     {}{}}%
    {{}%
     {Rumelt}{R.}%
     {Richard}{R.}%
     {}{}%
     {}{}}%
    {{}%
     {Dosi}{D.}%
     {Giovanni}{G.}%
     {}{}%
     {}{}}%
    {{}%
     {Winter}{W.}%
     {Sidney}{S.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{TDJ+1}
  \strng{fullhash}{TDJRRDGWS1}
  \field{abstract}{%
  Multiproduct firms are perceived to be coherent in their scope, yet there is
  no strong theoretical foundations to explain coherence in modern industrial
  organization theory. This paper shows that as U.S. manufacturing firms grow
  more diverse, they maintain a constant level of coherence between neighboring
  activities. This finding runs counter to the idea that firms with many
  activities are generally more ‘incoherent’. A framework is then presented
  which appeals to the nature of enterprise learning, path dependencies, and
  the nature of the selection environment to explain the ubiquity of coherent
  diversifiers.%
  }
  \verb{doi}
  \verb 10.1016/0167-2681(94)90094-9
  \endverb
  \field{issn}{0167-2681}
  \field{number}{1}
  \field{pages}{1\bibrangedash 30}
  \field{shortjournal}{Journal of Economic Behavior \& Organization}
  \field{shorttitle}{Understanding corporate coherence}
  \field{title}{Understanding corporate coherence: {{Theory}} and evidence}
  \verb{url}
  \verb http://www.sciencedirect.com/science/article/pii/0167268194900949
  \endverb
  \field{volume}{23}
  \verb{file}
  \verb ScienceDirect Snapshot:/Users/jeff/Library/Application Support/Zotero/P
  \verb rofiles/eaysq4eo.default/zotero/storage/93XGCHZ5/0167268194900949.html:
  \endverb
  \field{journaltitle}{Journal of Economic Behavior \& Organization}
  \field{month}{01}
  \field{year}{1994}
  \field{urlday}{31}
  \field{urlmonth}{10}
  \field{urlyear}{2014}
\endentry

\entry{Li2014}{article}{}
  \name{author}{8}{}{%
    {{}%
     {Li}{L.}%
     {Guan-Cheng}{G.-C.}%
     {}{}%
     {}{}}%
    {{}%
     {Lai}{L.}%
     {Ronald}{R.}%
     {}{}%
     {}{}}%
    {{}%
     {D’Amour}{D.}%
     {Alexander}{A.}%
     {}{}%
     {}{}}%
    {{}%
     {Doolin}{D.}%
     {David~M.}{D.~M.}%
     {}{}%
     {}{}}%
    {{}%
     {Sun}{S.}%
     {Ye}{Y.}%
     {}{}%
     {}{}}%
    {{}%
     {Torvik}{T.}%
     {Vetle~I.}{V.~I.}%
     {}{}%
     {}{}}%
    {{}%
     {Yu}{Y.}%
     {Amy~Z.}{A.~Z.}%
     {}{}%
     {}{}}%
    {{}%
     {Fleming}{F.}%
     {Lee}{L.}%
     {}{}%
     {}{}}%
  }
  \keyw{Bowen_To_Read,Jeff_Read}
  \strng{namehash}{LGC+1}
  \strng{fullhash}{LGCLRDADDMSYTVIYAZFL1}
  \verb{doi}
  \verb 10.1016/j.respol.2014.01.012
  \endverb
  \field{number}{6}
  \field{pages}{941\bibrangedash 955}
  \field{title}{Disambiguation and co-authorship networks of the {{U}}.{{S}}.
  patent inventor database (1975–2010)}
  \verb{url}
  \verb http://linkinghub.elsevier.com/retrieve/pii/S0048733314000225
  \endverb
  \field{volume}{43}
  \verb{file}
  \verb Li_et_al_2014.pdf:/Users/jeff/Library/Application Support/Zotero/Profil
  \verb es/eaysq4eo.default/zotero/storage/DFB8MKBK/Li_et_al_2014.pdf:applicati
  \verb on/pdf
  \endverb
  \field{journaltitle}{Research Policy}
  \field{month}{07}
  \field{year}{2014}
\endentry

\entry{Hall2001}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Hall}{H.}%
     {Bronwyn~H.}{B.~H.}%
     {}{}%
     {}{}}%
    {{}%
     {Ziedonis}{Z.}%
     {Rosemarie~Ham}{R.~H.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{HBHZRH1}
  \strng{fullhash}{HBHZRH1}
  \field{abstract}{%
  We examine the patenting behavior of firms in an industry characterized by
  rapid technological change and cumulative innovation. Recent survey evidence
  suggests that semiconductor firms do not rely heavily on patents to
  appropriate returns to R\&D. Yet the propensity of semiconductor firms to
  patent has risen dramatically since the mid-1980s. We explore this apparent
  paradox by conducting interviews with industry representatives and analyzing
  the patenting behavior of 95 U.S. semiconductor firms during 1979-1995. The
  results suggest that the 1980s strengthening of U.S. patent rights spawned
  "patent portfolio races" among capital-intensive firms, but it also
  facilitated entry by specialized design firms.%
  }
  \verb{doi}
  \verb 10.2307/2696400
  \endverb
  \verb{eprint}
  \verb 2696400
  \endverb
  \field{issn}{0741-6261}
  \field{number}{1}
  \field{pages}{101\bibrangedash 128}
  \field{shortjournal}{The RAND Journal of Economics}
  \field{shorttitle}{The {{Patent Paradox Revisited}}}
  \field{title}{The {{Patent Paradox Revisited}}: {{An Empirical Study}} of
  {{Patenting}} in the {{U}}.{{S}}. {{Semiconductor Industry}}, 1979-1995}
  \field{volume}{32}
  \verb{file}
  \verb JSTOR Full Text PDF:/Users/jeff/Library/Application Support/Zotero/Prof
  \verb iles/eaysq4eo.default/zotero/storage/QWCNJ6SU/Hall and Ziedonis - 2001
  \verb - The Patent Paradox Revisited An Empirical Study o.pdf:application/pdf
  \endverb
  \field{journaltitle}{The RAND Journal of Economics}
  \field{eprinttype}{jstor}
  \field{day}{01}
  \field{month}{04}
  \field{year}{2001}
\endentry

\entry{Criscuolo2008}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Criscuolo}{C.}%
     {Paola}{P.}%
     {}{}%
     {}{}}%
    {{}%
     {Verspagen}{V.}%
     {Bart}{B.}%
     {}{}%
     {}{}}%
  }
  \keyw{Local knowledge spillovers,Patent citations}
  \strng{namehash}{CPVB1}
  \strng{fullhash}{CPVB1}
  \field{abstract}{%
  This paper addresses the question of whether patent citations are useful
  indicators of technology flows. We exploit the distinction between citations
  added by inventors and patent examiners. We use information from the search
  reports of European Patent Office patent examiners to construct our dataset
  of patenting activity in Europe and the US, and apply various econometric
  models to investigate what determines the probability that a citation is
  added by the inventor rather than the examiner. Contrary to previous work
  which uses US Patent and Trademark Office data, we find that geographical
  distance is a factor that strongly diminishes the probability of knowledge
  flows. We find other significant effects of such factors as cognitive
  distance, time and strategic factors on citing behaviour.%
  }
  \verb{doi}
  \verb 10.1016/j.respol.2008.07.011
  \endverb
  \field{issn}{0048-7333}
  \field{number}{10}
  \field{pages}{1892\bibrangedash 1908}
  \field{series}{Special Section Knowledge Dynamics out of Balance: Knowledge
  Biased, Skewed and Unmatched}
  \field{shortjournal}{Research Policy}
  \field{shorttitle}{Does it matter where patent citations come from?}
  \field{title}{Does it matter where patent citations come from? {{Inventor}}
  vs. examiner citations in {{European}} patents}
  \verb{url}
  \verb http://www.sciencedirect.com/science/article/pii/S0048733308001583
  \endverb
  \field{volume}{37}
  \verb{file}
  \verb ScienceDirect Full Text PDF:/Users/jeff/Library/Application Support/Zot
  \verb ero/Profiles/eaysq4eo.default/zotero/storage/AVXXM66Z/Criscuolo and Ver
  \verb spagen - 2008 - Does it matter where patent citations come from I.pdf:a
  \verb pplication/pdf;ScienceDirect Snapshot:/Users/jeff/Library/Application S
  \verb upport/Zotero/Profiles/eaysq4eo.default/zotero/storage/QSJSJIBP/S004873
  \verb 3308001583.html:
  \endverb
  \field{journaltitle}{Research Policy}
  \field{month}{12}
  \field{year}{2008}
  \field{urlday}{15}
  \field{urlmonth}{07}
  \field{urlyear}{2015}
\endentry

\entry{Alcacer2006}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Alcácer}{A.}%
     {Juan}{J.}%
     {}{}%
     {}{}}%
    {{}%
     {Gittelman}{G.}%
     {Michelle}{M.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{AJGM1}
  \strng{fullhash}{AJGM1}
  \field{abstract}{%
  Analysis of patent citations is a core methodology in the study of knowledge
  diffusion. However, citations made by patent examiners have not been
  separately reported, adding unknown noise to the data. We leverage a recent
  change in the reporting of patent data showing citations added by examiners.
  The magnitude is high: two-thirds of citations on the average patent are
  inserted by examiners. Furthermore, 40\% of all patents have all citations
  added by examiners. We analyze the distribution of examiner and inventor
  citations with respect to self-citation, distance, technology overlap, and
  vintage. Results indicate that inferences about inventor knowledge using
  pooled citations may suffer from bias or overinflated significance levels.%
  }
  \verb{doi}
  \verb 10.1162/rest.88.4.774
  \endverb
  \field{issn}{0034-6535}
  \field{number}{4}
  \field{pages}{774\bibrangedash 779}
  \field{shortjournal}{Review of Economics and Statistics}
  \field{shorttitle}{Patent {{Citations}} as a {{Measure}} of {{Knowledge
  Flows}}}
  \field{title}{Patent {{Citations}} as a {{Measure}} of {{Knowledge Flows}}:
  {{The Influence}} of {{Examiner Citations}}}
  \verb{url}
  \verb http://dx.doi.org/10.1162/rest.88.4.774
  \endverb
  \field{volume}{88}
  \verb{file}
  \verb Review of Economics and Statistics Snapshot:/Users/jeff/Library/Applica
  \verb tion Support/Zotero/Profiles/eaysq4eo.default/zotero/storage/FJ8FNA54/r
  \verb est.88.4.html:
  \endverb
  \field{journaltitle}{Review of Economics and Statistics}
  \field{day}{01}
  \field{month}{11}
  \field{year}{2006}
  \field{urlday}{15}
  \field{urlmonth}{07}
  \field{urlyear}{2015}
\endentry

\entry{2014}{inreference}{}
  \keyw{Jeff_Read}
  \field{abstract}{%
  The NK model is a mathematical model described by its primary inventor Stuart
  Kauffman as a "tunably rugged" fitness landscape. "Tunable ruggedness"
  captures the intuition that both the overall size of the landscape and the
  number of its local "hills and valleys" can be adjusted via changes to its
  two parameters, and , defined below. The NK model has found application in a
  wide variety of fields, including the theoretical study of evolutionary
  biology, immunology, optimisation and complex systems. The model was also
  adopted in organizational theory, where it is used to describe the way an
  agent may search a landscape by manipulating various characteristics of
  itself. For example, an agent can be an organization, the hills and valleys
  represent profit (or changes thereof), and movement on the landscape
  necessitates organizational decisions (such as adding product lines or
  altering the organizational structure), which tend to interact with each
  other and affect profit in a complex fashion.[1]%
  }
  \field{booktitle}{Wikipedia, the free encyclopedia}
  \field{note}{Page Version ID: 604943239}
  \field{title}{{{NK}} model}
  \verb{url}
  \verb http://en.wikipedia.org/w/index.php?title=NK_model&oldid=604943239
  \endverb
  \field{langid}{english}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/IMEA8MZ6/2014 - NK model.html:
  \endverb
  \field{day}{24}
  \field{month}{08}
  \field{year}{2014}
  \field{urlday}{17}
  \field{urlmonth}{09}
  \field{urlyear}{2014}
\endentry

\entry{Valverde2007}{article}{}
  \name{author}{4}{}{%
    {{}%
     {Valverde}{V.}%
     {Sergi}{S.}%
     {}{}%
     {}{}}%
    {{}%
     {Sol}{S.}%
     {Ricard~V}{R.~V.}%
     {}{}%
     {}{}}%
    {{}%
     {Bedau}{B.}%
     {Mark~A}{M.~A.}%
     {}{}%
     {}{}}%
    {{}%
     {Packard}{P.}%
     {Norman}{N.}%
     {}{}%
     {}{}}%
  }
  \keyw{Jeff_To_Read}
  \strng{namehash}{VS+1}
  \strng{fullhash}{VSSRVBMAPN1}
  \field{title}{Topology and {{Evolution}} of {{Technology Innovation
  Networks}}}
  \field{volume}{76}
  \verb{file}
  \verb Valverde_et_al_2007.pdf:/Users/jeff/Library/Application Support/Zotero/
  \verb Profiles/eaysq4eo.default/zotero/storage/29WIBQWU/Valverde_et_al_2007.p
  \verb df:application/pdf
  \endverb
  \field{journaltitle}{Physical Review E}
  \field{year}{2007}
\endentry

\entry{Eck2009}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Eck}{E.}%
     {Nees~Jan}{N.~J.}%
     {van}{v.}%
     {}{}}%
    {{}%
     {Waltman}{W.}%
     {Ludo}{L.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{ENJvWL1}
  \strng{fullhash}{ENJvWL1}
  \field{abstract}{%
  In scientometric research, the use of cooccurrence data is very common. In
  many cases, a similarity measure is employed to normalize the data. However,
  there is no consensus among researchers on which similarity measure is most
  appropriate for normalization purposes. In this article, we theoretically
  analyze the properties of similarity measures for cooccurrence data, focusing
  in particular on four well-known measures: the association strength, the
  cosine, the inclusion index, and the Jaccard index. We also study the
  behavior of these measures empirically. Our analysis reveals that there exist
  two fundamentally different types of similarity measures, namely,
  set-theoretic measures and probabilistic measures. The association strength
  is a probabilistic measure, while the cosine, the inclusion index, and the
  Jaccard index are set-theoretic measures. Both our theoretical and our
  empirical results indicate that cooccurrence data can best be normalized
  using a probabilistic measure. This provides strong support for the use of
  the association strength in scientometric research.%
  }
  \verb{doi}
  \verb 10.1002/asi.21075
  \endverb
  \field{issn}{1532-2890}
  \field{number}{8}
  \field{pages}{1635\bibrangedash 1651}
  \field{shortjournal}{J. Am. Soc. Inf. Sci.}
  \field{shorttitle}{How to normalize cooccurrence data?}
  \field{title}{How to normalize cooccurrence data? {{An}} analysis of some
  well-known similarity measures}
  \verb{url}
  \verb http://onlinelibrary.wiley.com/doi/10.1002/asi.21075/abstract
  \endverb
  \field{volume}{60}
  \field{langid}{english}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/7P4VG6K2/abstract.html:
  \endverb
  \field{journaltitle}{Journal of the American Society for Information Science
  and Technology}
  \field{day}{01}
  \field{month}{08}
  \field{year}{2009}
  \field{urlday}{18}
  \field{urlmonth}{05}
  \field{urlyear}{2015}
\endentry

\entry{Ulrich2007}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Ulrich}{U.}%
     {Werner}{W.}%
     {}{}%
     {}{}}%
    {{}%
     {J.~Gotelli}{J.~G.}%
     {Nicholas}{N.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{UWJGN1}
  \strng{fullhash}{UWJGN1}
  \field{abstract}{%
  Two opposing patterns of meta-community organization are nestedness and
  negative species co-occurrence. Both patterns can be quantified with metrics
  that are applied to presence-absence matrices and tested with null model
  analysis. Previous meta-analyses have given conflicting results, with the
  same set of matrices apparently showing high nestedness (Wright et al. 1998)
  and negative species co-occurrence (Gotelli and McCabe 2002). We clarified
  the relationship between nestedness and co-occurrence by creating random
  matrices, altering them systematically to increase or decrease the degree of
  nestedness or co-occurrence, and then testing the resulting patterns with
  null models. Species co-occurrence is related to the degree of nestedness,
  but the sign of the relationship depends on how the test matrices were
  created. Low-fill matrices created by simple, uniform sampling generate
  negative correlations between nestedness and co-occurrence: negative species
  co-occurrence is associated with disordered matrices. However, high-fill
  matrices created by passive sampling generate the opposite pattern: negative
  species co-occurrence is associated with highly nested matrices. The patterns
  depend on which index of species co-occurrence is used, and they are not
  symmetric: systematic changes in the co-occurrence structure of a matrix are
  only weakly associated with changes in the pattern of nestedness. In all
  analyses, the fixed-fixed null model that preserves matrix row and column
  totals has lower type I and type II error probabilities than an equiprobable
  null model that relaxes row and column totals. The latter model is part of
  the popular nestedness temperature calculator, which detects nestedness too
  frequently in random matrices (type I statistical error). When compared to a
  valid null model, a matrix with negative species co-occurrence may be either
  highly nested or disordered, depending on the biological processes that
  determine row totals (number of species occurrences) and column totals
  (number of species per site).%
  }
  \verb{doi}
  \verb 10.1111/j.2007.0030-1299.16173.x
  \endverb
  \field{issn}{1600-0706}
  \field{number}{12}
  \field{pages}{2053\bibrangedash 2061}
  \field{shortjournal}{Oikos}
  \field{title}{Disentangling community patterns of nestedness and species
  co-occurrence}
  \verb{url}
  \verb http://onlinelibrary.wiley.com/doi/10.1111/j.2007.0030-1299.16173.x/abs
  \verb tract
  \endverb
  \field{volume}{116}
  \field{langid}{english}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/PV8T7DTZ/abstract.html:
  \endverb
  \field{journaltitle}{Oikos}
  \field{day}{01}
  \field{month}{12}
  \field{year}{2007}
  \field{urlday}{11}
  \field{urlmonth}{07}
  \field{urlyear}{2015}
\endentry

\entry{Stone1990}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Stone}{S.}%
     {Lewi}{L.}%
     {}{}%
     {}{}}%
    {{}%
     {Roberts}{R.}%
     {Alan}{A.}%
     {}{}%
     {}{}}%
  }
  \keyw{Bird distributions,Checkerboard pattern,community
  structure,Ecology,Plant Sciences,random colonisation,Species interaction}
  \strng{namehash}{SLRA1}
  \strng{fullhash}{SLRA1}
  \field{abstract}{%
  There has been an ongoing controversy over how to decide whether the
  distribution of species is “random” — i.e., whether it is not greatly
  different from what it would be if species did not interact. We recently
  showed (Roberts and Stone (1990)) that in the case of the Vanuatu (formerly
  New Hebrides) avifauna, the number of islands shared by species pairs was
  incompatible with a “random” null hypothesis. However, it was difficult
  to determine the causes or direction of the community's exceptionality. In
  this paper, the latter problem is examined further. We use Diamond's (1975)
  notion of checkerboard distributions (originally developed as an indicator of
  competition) and construct a C-score statistic which quantifies
  “checkerboardedness”. This statistic is based on the way two species
  might colonise a pair of islands; whenever each species colonises a different
  island this adds 1 to the C-score. Following Connor and Simberloff (1979) we
  generate a “control group” of random colonisation patterns (matrices),
  and use the C-score to determine their checkerboard characteristics. As an
  alternative mode of enquiry, we make slight alterations to the observed data,
  repeating this process many times so as to obtain another “control
  group”. In both cases, when we compare the observed data for the Vanuatu
  avifauna and the Antillean bat communities with that given by their
  respective “control group”, we find that these communities have
  significantly large checkerboard distributions, making implausible the
  hypothesis that their species distributions are a product of random
  colonisation.%
  }
  \verb{doi}
  \verb 10.1007/BF00317345
  \endverb
  \field{issn}{0029-8549, 1432-1939}
  \field{number}{1}
  \field{pages}{74\bibrangedash 79}
  \field{shortjournal}{Oecologia}
  \field{title}{The checkerboard score and species distributions}
  \verb{url}
  \verb http://link.springer.com/article/10.1007/BF00317345
  \endverb
  \field{volume}{85}
  \field{langid}{english}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/PRAK6NPW/BF00317345.html:
  \endverb
  \field{journaltitle}{Oecologia}
  \field{month}{11}
  \field{year}{1990}
  \field{urlday}{11}
  \field{urlmonth}{07}
  \field{urlyear}{2015}
\endentry

\entry{Gobbi2014}{article}{}
  \name{author}{10}{}{%
    {{}%
     {Gobbi}{G.}%
     {Andrea}{A.}%
     {}{}%
     {}{}}%
    {{}%
     {Iorio}{I.}%
     {Francesco}{F.}%
     {}{}%
     {}{}}%
    {{}%
     {Dawson}{D.}%
     {Kevin~J.}{K.~J.}%
     {}{}%
     {}{}}%
    {{}%
     {Wedge}{W.}%
     {David~C.}{D.~C.}%
     {}{}%
     {}{}}%
    {{}%
     {Tamborero}{T.}%
     {David}{D.}%
     {}{}%
     {}{}}%
    {{}%
     {Alexandrov}{A.}%
     {Ludmil~B.}{L.~B.}%
     {}{}%
     {}{}}%
    {{}%
     {Lopez-Bigas}{L.-B.}%
     {Nuria}{N.}%
     {}{}%
     {}{}}%
    {{}%
     {Garnett}{G.}%
     {Mathew~J.}{M.~J.}%
     {}{}%
     {}{}}%
    {{}%
     {Jurman}{J.}%
     {Giuseppe}{G.}%
     {}{}%
     {}{}}%
    {{}%
     {Saez-Rodriguez}{S.-R.}%
     {Julio}{J.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{GA+1}
  \strng{fullhash}{GAIFDKJWDCTDALBLBNGMJJGSRJ1}
  \field{abstract}{%
  Motivation: Studying combinatorial patterns in cancer genomic datasets has
  recently emerged as a tool for identifying novel cancer driver networks.
  Approaches have been devised to quantify, for example, the tendency of a set
  of genes to be mutated in a ‘mutually exclusive’ manner. The significance
  of the proposed metrics is usually evaluated by computing P-values under
  appropriate null models. To this end, a Monte Carlo method (the
  switching-algorithm) is used to sample simulated datasets under a null model
  that preserves patient- and gene-wise mutation rates. In this method, a
  genomic dataset is represented as a bipartite network, to which Markov chain
  updates (switching-steps) are applied. These steps modify the network
  topology, and a minimal number of them must be executed to draw simulated
  datasets independently under the null model. This number has previously been
  deducted empirically to be a linear function of the total number of variants,
  making this process computationally expensive. Results: We present a novel
  approximate lower bound for the number of switching-steps, derived
  analytically. Additionally, we have developed the R package BiRewire,
  including new efficient implementations of the switching-algorithm. We
  illustrate the performances of BiRewire by applying it to large real cancer
  genomics datasets. We report vast reductions in time requirement, with
  respect to existing implementations/bounds and equivalent P-value
  computations. Thus, we propose BiRewire to study statistical properties in
  genomic datasets, and other data that can R;ampD be modeled as bipartite
  networks. Availability and implementation: BiRewire is available on
  BioConductor at
  http://www.bioconductor.org/packages/2.13/bioc/html/BiRewire.html Contact:
  iorio@ebi.ac.uk Supplementary information: Supplementary data are available
  at Bioinformatics online.%
  }
  \verb{doi}
  \verb 10.1093/bioinformatics/btu474
  \endverb
  \verb{eprint}
  \verb 25161255
  \endverb
  \field{issn}{1367-4803, 1460-2059}
  \field{number}{17}
  \field{pages}{i617\bibrangedash i623}
  \field{shortjournal}{Bioinformatics}
  \field{title}{Fast randomization of large genomic datasets while preserving
  alteration counts}
  \verb{url}
  \verb http://bioinformatics.oxfordjournals.org/content/30/17/i617
  \endverb
  \field{volume}{30}
  \field{langid}{english}
  \verb{file}
  \verb Gobbi_et_al_2014.pdf:/Users/jeff/Library/Application Support/Zotero/Pro
  \verb files/eaysq4eo.default/zotero/storage/95T67NHN/Gobbi_et_al_2014.pdf:app
  \verb lication/pdf;Snapshot:/Users/jeff/Library/Application Support/Zotero/Pr
  \verb ofiles/eaysq4eo.default/zotero/storage/V92U3SRU/i617.html:
  \endverb
  \field{journaltitle}{Bioinformatics}
  \field{eprinttype}{pmid}
  \field{day}{09}
  \field{month}{01}
  \field{year}{2014}
  \field{urlday}{10}
  \field{urlmonth}{07}
  \field{urlyear}{2015}
\endentry

\entry{Neffke2008}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Neffke}{N.}%
     {Frank}{F.}%
     {}{}%
     {}{}}%
    {{}%
     {Svensson~Henning}{S.~H.}%
     {Martin}{M.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{NFSHM1}
  \strng{fullhash}{NFSHM1}
  \field{pages}{19}
  \field{shorttitle}{Revealed relatedness}
  \field{title}{Revealed relatedness: {{Mapping}} industry space}
  \verb{url}
  \verb http://www2.druid.dk/conferences/viewpaper.php?id=3691&cf=29
  \endverb
  \field{volume}{8}
  \verb{file}
  \verb Neffke & Henning, 2008.pdf:/Users/jeff/Library/Application Support/Zote
  \verb ro/Profiles/eaysq4eo.default/zotero/storage/VWUVCZSA/Neffke & Henning,
  \verb 2008.pdf:application/pdf
  \endverb
  \field{journaltitle}{Papers in Evolutionary Economic Geography}
  \field{year}{2008}
  \field{urlday}{18}
  \field{urlmonth}{10}
  \field{urlyear}{2014}
\endentry

\entry{Saviotti1995}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Saviotti}{S.}%
     {P.~P.}{P.~P.}%
     {}{}%
     {}{}}%
    {{}%
     {Mani}{M.}%
     {G.~S.}{G.~S.}%
     {}{}%
     {}{}}%
  }
  \keyw{Competition,Economics general,Economic Theory,International
  Economics,O3,O30,Replicator,Technological,variety}
  \strng{namehash}{SPPMGS1}
  \strng{fullhash}{SPPMGS1}
  \field{abstract}{%
  In this paper a model of technological evolution based on replicator dynamics
  is developed. Such a model is based on a twin characteristics representation
  of product technology and on a population approach. The model can give a
  general representation of technological evolution, but this paper
  concentrates on the relationship between variety and competition. Variety is
  considered to be a very important variable influencing economic development.
  By means of the characteristics and population approach adopted in this paper
  it is possible to distinguish between inter-and intra-technology competition.
  In this paper it is demonstrated that the variety of the system can only
  increase if intra-technology competition is more intense than
  inter-technology competition. Intuitively this implies that new technologies
  will only be able to emerge if incumbent technologies experience the
  competition coming from the new ones to be weaker than their own internal
  competition.%
  }
  \verb{doi}
  \verb 10.1007/BF01194367
  \endverb
  \field{issn}{0936-9937, 1432-1386}
  \field{number}{4}
  \field{pages}{369\bibrangedash 392}
  \field{shortjournal}{J Evol Econ}
  \field{shorttitle}{Competition, variety and technological evolution}
  \field{title}{Competition, variety and technological evolution: {{A}}
  replicator dynamics model}
  \verb{url}
  \verb http://link.springer.com/article/10.1007/BF01194367
  \endverb
  \field{volume}{5}
  \field{langid}{english}
  \field{journaltitle}{Journal of Evolutionary Economics}
  \field{month}{12}
  \field{year}{1995}
  \field{urlday}{16}
  \field{urlmonth}{04}
  \field{urlyear}{2016}
\endentry

\entry{Bruckner1994}{article}{}
  \name{author}{4}{}{%
    {{}%
     {Bruckner}{B.}%
     {Eberhard}{E.}%
     {}{}%
     {}{}}%
    {{}%
     {Ebeling}{E.}%
     {Werner}{W.}%
     {}{}%
     {}{}}%
    {{}%
     {Montaño}{M.}%
     {Miguel A.~Jiménez}{M.~A.~J.}%
     {}{}%
     {}{}}%
    {{}%
     {Scharnhorst}{S.}%
     {Andrea}{A.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{BE+1}
  \strng{fullhash}{BEEWMMAJSA1}
  \field{pages}{79\bibrangedash 90}
  \field{title}{Hyperselection and innovation described by a stochastic model
  of technological evolution}
  \field{journaltitle}{Evolutionary economics and chaos theory: New directions
  in technology studies}
  \field{year}{1994}
\endentry

\entry{Verspagen1997}{article}{}
  \name{author}{1}{}{%
    {{}%
     {Verspagen}{V.}%
     {Bart}{B.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{VB1}
  \strng{fullhash}{VB1}
  \field{abstract}{%
  This paper presents several new methods for measuring intersectoral knowledge
  spillovers, and applies these methods in an analysis of productivity growth
  in manufacturing for a cross-country, cross-sectional sample for the 1980s.
  It is argued that existing methods of measuring such intersectoral knowledge
  spillovers are mostly aimed at measuring so-called crent spillovers'. The
  methods developed here are aimed at measuring knowledge spillovers—an
  additional aspect of the spillover process. The empirical analysis shows that
  there are indeed differences between these two types of spillover measure.%
  }
  \verb{doi}
  \verb 10.1080/09535319700000004
  \endverb
  \field{issn}{0953-5314}
  \field{number}{1}
  \field{pages}{47\bibrangedash 65}
  \field{shorttitle}{Measuring {{Intersectoral Technology Spillovers}}}
  \field{title}{Measuring {{Intersectoral Technology Spillovers}}:
  {{Estimates}} from the {{European}} and {{US Patent Office Databases}}}
  \verb{url}
  \verb http://dx.doi.org/10.1080/09535319700000004
  \endverb
  \field{volume}{9}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/9GSVM2VZ/09535319700000004.html:
  \endverb
  \field{journaltitle}{Economic Systems Research}
  \field{day}{01}
  \field{month}{03}
  \field{year}{1997}
  \field{urlday}{18}
  \field{urlmonth}{05}
  \field{urlyear}{2015}
\endentry

\entry{Bloom2013}{article}{}
  \name{author}{3}{}{%
    {{}%
     {Bloom}{B.}%
     {Nicholas}{N.}%
     {}{}%
     {}{}}%
    {{}%
     {Schankerman}{S.}%
     {Mark}{M.}%
     {}{}%
     {}{}}%
    {{}%
     {Van~Reenen}{V.~R.}%
     {John}{J.}%
     {}{}%
     {}{}}%
  }
  \keyw{market value,Patents,productivity,R&D,Spillovers}
  \strng{namehash}{BNSMVRJ1}
  \strng{fullhash}{BNSMVRJ1}
  \field{abstract}{%
  The impact of R\&D on growth through spillovers has been a major topic of
  economic research over the last thirty years. A central problem in the
  literature is that firm performance is affected by two countervailing
  “spillovers” : a positive effect from technology (knowledge) spillovers
  and a negative business stealing effects from product market rivals. We
  develop a general framework incorporating these two types of spillovers and
  implement this model using measures of a firm's position in technology space
  and productmarket space. Using panel data on U.S. firms, we show that
  technology spillovers quantitatively dominate, so that the gross social
  returns to R\&D are at least twice as high as the private returns. We
  identify the causal effect of R\&D spillovers by using changes in federal and
  state tax incentives for R\&D. We also find that smaller firms generate lower
  social returns to R\&D because they operate more in technological niches.
  Finally, we detail the desirable properties of an ideal spillover measure and
  how existing approaches, including our new Mahalanobis measure, compare to
  these criteria.%
  }
  \verb{doi}
  \verb 10.3982/ECTA9466
  \endverb
  \field{issn}{1468-0262}
  \field{number}{4}
  \field{pages}{1347\bibrangedash 1393}
  \field{title}{Identifying {{Technology Spillovers}} and {{Product Market
  Rivalry}}}
  \verb{url}
  \verb http://onlinelibrary.wiley.com/doi/10.3982/ECTA9466/abstract
  \endverb
  \field{volume}{81}
  \field{langid}{english}
  \field{journaltitle}{Econometrica}
  \field{day}{01}
  \field{month}{07}
  \field{year}{2013}
  \field{urlday}{16}
  \field{urlmonth}{04}
  \field{urlyear}{2016}
\endentry

\entry{Fleming2001}{article}{}
  \name{author}{1}{}{%
    {{}%
     {Fleming}{F.}%
     {Lee}{L.}%
     {}{}%
     {}{}}%
  }
  \keyw{Jeff_Read}
  \strng{namehash}{FL1}
  \strng{fullhash}{FL1}
  \verb{doi}
  \verb 10.1287/mnsc.47.1.117.10671
  \endverb
  \field{number}{1}
  \field{pages}{117\bibrangedash 132}
  \field{title}{Recombinant {{Uncertainty}} in {{Technological Search}}}
  \verb{url}
  \verb http://pubsonline.informs.org/doi/abs/10.1287/mnsc.47.1.117.10671
  \endverb
  \field{volume}{47}
  \verb{file}
  \verb Fleming_2001.pdf:/Users/jeff/Library/Application Support/Zotero/Profile
  \verb s/eaysq4eo.default/zotero/storage/3RA6KFAR/Fleming_2001.pdf:application
  \verb /pdf
  \endverb
  \field{journaltitle}{Management Science}
  \field{month}{01}
  \field{year}{2001}
\endentry

\entry{Fleming2007}{article}{}
  \name{author}{1}{}{%
    {{}%
     {Fleming}{F.}%
     {Lee}{L.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{FL1}
  \strng{fullhash}{FL1}
  \field{number}{1}
  \field{title}{Breakthroughs and the “{{Long Tail}}” of {{Innovation}}}
  \field{volume}{49}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/6SA8WI68/breakthroughs-and-the-long-tail-of-i
  \verb nnovation.html:;Technology_reserved_.pdf:/Users/jeff/Library/Applicatio
  \verb n Support/Zotero/Profiles/eaysq4eo.default/zotero/storage/EB9J67W6/Tech
  \verb nology_reserved_.pdf:application/pdf
  \endverb
  \field{journaltitle}{MIT Sloan Management Review}
  \field{year}{2007}
\endentry

\entry{Simonton1999}{article}{}
  \name{author}{1}{}{%
    {{}%
     {Simonton}{S.}%
     {Dean~Keith}{D.~K.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{SDK1}
  \strng{fullhash}{SDK1}
  \field{abstract}{%
  Darwinism provides not only a theory of biological evolution but also
  supplies a more generic process applicable to many phenomena in the
  behavioral sciences. Among these applications is the blind-variation and
  selective-retention model of creativity proposed by Campbell (1960). Research
  over the past 4 decades lends even more support to Campbell's model. This
  support is indicated by reviewing the experimental, psychometric, and
  historiometric literature on creativity. Then 4 major objections to the
  Darwinian model are examined (sociocultural determinism, individual volition,
  human rationality, and domain expertise). The article concludes by
  speculating whether the Darwinian model may actually subsume all alternative
  theories of creativity as special cases of the larger framework.%
  }
  \verb{eprint}
  \verb 1449455
  \endverb
  \field{issn}{1047-840X}
  \field{number}{4}
  \field{pages}{309\bibrangedash 328}
  \field{shortjournal}{Psychological Inquiry}
  \field{shorttitle}{Creativity as {{Blind Variation}} and {{Selective
  Retention}}}
  \field{title}{Creativity as {{Blind Variation}} and {{Selective Retention}}:
  {{Is}} the {{Creative Process Darwinian}}?}
  \field{volume}{10}
  \field{journaltitle}{Psychological Inquiry}
  \field{eprinttype}{jstor}
  \field{year}{1999}
\endentry

\entry{Chan2011}{article}{}
  \name{author}{6}{}{%
    {{}%
     {Chan}{C.}%
     {Joel}{J.}%
     {}{}%
     {}{}}%
    {{}%
     {Fu}{F.}%
     {Katherine}{K.}%
     {}{}%
     {}{}}%
    {{}%
     {Schunn}{S.}%
     {Christian}{C.}%
     {}{}%
     {}{}}%
    {{}%
     {Cagan}{C.}%
     {Jonathan}{J.}%
     {}{}%
     {}{}}%
    {{}%
     {Wood}{W.}%
     {Kristin}{K.}%
     {}{}%
     {}{}}%
    {{}%
     {Kotovsky}{K.}%
     {Kenneth}{K.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{CJ+1}
  \strng{fullhash}{CJFKSCCJWKKK1}
  \field{abstract}{%
  Drawing inspiration from examples by analogy can be a powerful tool for
  innovative design during conceptual ideation but also carries the risk of
  negative design outcomes (e.g., design fixation), depending on key properties
  of examples. Understanding these properties is critical for effectively
  harnessing the power of analogy. The current research explores how variations
  in analogical distance, commonness, and representation modality influence the
  effects of examples on conceptual ideation. Senior-level engineering students
  generated solution concepts for an engineering design problem with or without
  provided examples drawn from the U.S. Patent database. Examples were crossed
  by analogical distance (near-field vs. far-field), commonness (more vs.
  less-common), and modality (picture vs. text). A control group that received
  no examples was included for comparison. Effects were examined on a mixture
  of ideation process and product variables. Our results show positive effects
  of far-field and less-common examples on novelty and variability in quality
  of solution concepts. These effects are not modulated by modality. However,
  detailed analyses of process variables suggest divergent inspiration pathways
  for far-field vs. less-common examples. Additionally, the combination of
  far-field, less-common examples resulted in more novel concepts than in the
  control group. These findings suggest guidelines for the effective design and
  implementation of design-by-analogy methods, particularly a focus on
  far-field, less-common examples during the ideation process.%
  }
  \verb{doi}
  \verb 10.1115/1.4004396
  \endverb
  \field{issn}{1050-0472}
  \field{number}{8}
  \field{pages}{081004\bibrangedash 081004}
  \field{shortjournal}{J. Mech. Des.}
  \field{shorttitle}{On the {{Benefits}} and {{Pitfalls}} of {{Analogies}} for
  {{Innovative Design}}}
  \field{title}{On the {{Benefits}} and {{Pitfalls}} of {{Analogies}} for
  {{Innovative Design}}: {{Ideation Performance Based}} on {{Analogical
  Distance}}, {{Commonness}}, and {{Modality}} of {{Examples}}}
  \verb{url}
  \verb http://dx.doi.org/10.1115/1.4004396
  \endverb
  \field{volume}{133}
  \verb{file}
  \verb Chan_et_al_2011.pdf:/Users/jeff/Library/Application Support/Zotero/Prof
  \verb iles/eaysq4eo.default/zotero/storage/XSVTDRXB/Chan_et_al_2011.pdf:appli
  \verb cation/pdf
  \endverb
  \field{journaltitle}{Journal of Mechanical Design}
  \field{day}{01}
  \field{month}{08}
  \field{year}{2011}
  \field{urlday}{04}
  \field{urlmonth}{02}
  \field{urlyear}{2015}
\endentry

\entry{Chan2015}{article}{}
  \name{author}{3}{}{%
    {{}%
     {Chan}{C.}%
     {Joel}{J.}%
     {}{}%
     {}{}}%
    {{}%
     {Dow}{D.}%
     {Steven~P.}{S.~P.}%
     {}{}%
     {}{}}%
    {{}%
     {Schunn}{S.}%
     {Christian~D.}{C.~D.}%
     {}{}%
     {}{}}%
  }
  \keyw{conceptual design,creative design,design cognition,Innovation,sources
  of inspiration}
  \strng{namehash}{CJDSPSCD1}
  \strng{fullhash}{CJDSPSCD1}
  \field{abstract}{%
  Design ideas often come from sources of inspiration (e.g., analogous designs,
  prior experiences). In this paper, we test the popular but unevenly supported
  hypothesis that conceptually distant sources of inspiration provide the best
  insights for creative production. Through text analysis of hundreds of design
  concepts across a dozen different design challenges on a Web-based innovation
  platform that tracks connections to sources of inspiration, we find that
  citing sources is associated with greater creativity of ideas, but
  conceptually closer rather than farther sources appear more beneficial. This
  inverse relationship between conceptual distance and design creativity is
  robust across different design problems on the platform. In light of these
  findings, we revisit theories of design inspiration and creative cognition.%
  }
  \verb{doi}
  \verb 10.1016/j.destud.2014.08.001
  \endverb
  \field{issn}{0142-694X}
  \field{pages}{31\bibrangedash 58}
  \field{shortjournal}{Design Studies}
  \field{title}{Do the best design ideas (really) come from conceptually
  distant sources of inspiration?}
  \verb{url}
  \verb http://www.sciencedirect.com/science/article/pii/S0142694X14000611
  \endverb
  \field{volume}{36}
  \verb{file}
  \verb ScienceDirect Snapshot:/Users/jeff/Library/Application Support/Zotero/P
  \verb rofiles/eaysq4eo.default/zotero/storage/6MIJR8J8/S0142694X14000611.html
  \verb :;Chan et al_2015.pdf:/Users/jeff/Library/Application Support/Zotero/Pr
  \verb ofiles/eaysq4eo.default/zotero/storage/N86EUJ56/Chan et al_2015.pdf:app
  \verb lication/pdf
  \endverb
  \field{journaltitle}{Design Studies}
  \field{month}{01}
  \field{year}{2015}
  \field{urlday}{19}
  \field{urlmonth}{04}
  \field{urlyear}{2016}
\endentry

\entry{Newman2004}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Newman}{N.}%
     {M.~E.~J.}{M.~E.~J.}%
     {}{}%
     {}{}}%
    {{}%
     {Girvan}{G.}%
     {M.}{M.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{NMEJGM1}
  \strng{fullhash}{NMEJGM1}
  \field{abstract}{%
  We propose and study a set of algorithms for discovering community structure
  in networks—natural divisions of network nodes into densely connected
  subgroups. Our algorithms all share two definitive features: first, they
  involve iterative removal of edges from the network to split it into
  communities, the edges removed being identified using any one of a number of
  possible “betweenness” measures, and second, these measures are,
  crucially, recalculated after each removal. We also propose a measure for the
  strength of the community structure found by our algorithms, which gives us
  an objective metric for choosing the number of communities into which a
  network should be divided. We demonstrate that our algorithms are highly
  effective at discovering community structure in both computer-generated and
  real-world network data, and show how they can be used to shed light on the
  sometimes dauntingly complex structure of networked systems.%
  }
  \verb{doi}
  \verb 10.1103/PhysRevE.69.026113
  \endverb
  \field{number}{2}
  \field{pages}{026113}
  \field{shortjournal}{Phys. Rev. E}
  \field{title}{Finding and evaluating community structure in networks}
  \verb{url}
  \verb http://link.aps.org/doi/10.1103/PhysRevE.69.026113
  \endverb
  \field{volume}{69}
  \verb{file}
  \verb APS Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/ea
  \verb ysq4eo.default/zotero/storage/8ZWPPWGI/PhysRevE.69.html:
  \endverb
  \field{journaltitle}{Physical Review E}
  \field{day}{26}
  \field{month}{02}
  \field{year}{2004}
  \field{urlday}{27}
  \field{urlmonth}{05}
  \field{urlyear}{2015}
\endentry

\entry{Blondel2008}{article}{}
  \name{author}{4}{}{%
    {{}%
     {Blondel}{B.}%
     {Vincent~D.}{V.~D.}%
     {}{}%
     {}{}}%
    {{}%
     {Guillaume}{G.}%
     {Jean-Loup}{J.-L.}%
     {}{}%
     {}{}}%
    {{}%
     {Lambiotte}{L.}%
     {Renaud}{R.}%
     {}{}%
     {}{}}%
    {{}%
     {Lefebvre}{L.}%
     {Etienne}{E.}%
     {}{}%
     {}{}}%
  }
  \keyw{networks,new applications of statistical mechanics,random graphs}
  \strng{namehash}{BVD+1}
  \strng{fullhash}{BVDGJLLRLE1}
  \field{abstract}{%
  We propose a simple method to extract the community structure of large
  networks. Our method is a heuristic method that is based on modularity
  optimization. It is shown to outperform all other known community detection
  methods in terms of computation time. Moreover, the quality of the
  communities detected is very good, as measured by the so-called modularity.
  This is shown first by identifying language communities in a Belgian mobile
  phone network of 2 million customers and by analysing a web graph of 118
  million nodes and more than one billion links. The accuracy of our algorithm
  is also verified on ad hoc modular networks.%
  }
  \verb{doi}
  \verb 10.1088/1742-5468/2008/10/P10008
  \endverb
  \field{issn}{1742-5468}
  \field{number}{10}
  \field{pages}{P10008}
  \field{shortjournal}{J. Stat. Mech.}
  \field{title}{Fast unfolding of communities in large networks}
  \verb{url}
  \verb http://iopscience.iop.org/1742-5468/2008/10/P10008
  \endverb
  \field{volume}{2008}
  \field{langid}{english}
  \verb{file}
  \verb Snapshot:/Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4
  \verb eo.default/zotero/storage/745G2R74/P10008.html:
  \endverb
  \field{journaltitle}{Journal of Statistical Mechanics: Theory and Experiment}
  \field{day}{01}
  \field{month}{10}
  \field{year}{2008}
  \field{urlday}{27}
  \field{urlmonth}{05}
  \field{urlyear}{2015}
\endentry

\entry{Traag2015}{article}{}
  \name{author}{1}{}{%
    {{}%
     {Traag}{T.}%
     {V.~A.}{V.~A.}%
     {}{}%
     {}{}}%
  }
  \keyw{Computer Science - Data Structures and Algorithms,Computer Science -
  Social and Information Networks,Physics - Physics and Society}
  \strng{namehash}{TVA1}
  \strng{fullhash}{TVA1}
  \field{abstract}{%
  Many complex networks exhibit a modular structure of densely connected groups
  of nodes. Usually, such a modular structure is uncovered by the optimisation
  of some quality function. Although flawed, Modularity remains one of the most
  popular quality functions. The Louvain algorithm was originally developed for
  optimising Modularity, but has been applied to a variety of methods. As such,
  speeding up the Louvain algorithm, enables the analysis of larger graphs in a
  shorter time for various methods. We here suggest to consider moving nodes to
  the community of a random neighbour, instead of the best neighbouring
  community. Although incredibly simple, it reduces the theoretical runtime
  complexity from \textdollar{}O(m)\textdollar{} to \textdollar{}O(n
  \ensuremath{\backslash}log \ensuremath{<}k\ensuremath{>})\textdollar{} in
  networks with a clear community structure. In benchmark networks, resembling
  real networks more closely, it speeds up the algorithm roughly 2-3 times.
  This is due to two factors: (1) a random neighbour is likely to be in a
  "good" community; and (2) random neighbours are likely to be hubs, helping
  the convergence. Finally, the performance gain only slightly diminishes the
  quality, thus providing an excellent quality-performance ratio. However,
  these gains do not seem to hold up when detecting small communities in large
  graphs.%
  }
  \verb{eprint}
  \verb 1503.01322
  \endverb
  \field{shorttitle}{Faster unfolding of communities}
  \field{title}{Faster unfolding of communities: speeding up the {{Louvain}}
  algorithm}
  \verb{url}
  \verb http://arxiv.org/abs/1503.01322
  \endverb
  \verb{file}
  \verb arXiv.org Snapshot:/Users/jeff/Library/Application Support/Zotero/Profi
  \verb les/eaysq4eo.default/zotero/storage/3GF2KUAI/1503.html:;Traag_2015.pdf:
  \verb /Users/jeff/Library/Application Support/Zotero/Profiles/eaysq4eo.defaul
  \verb t/zotero/storage/DPCTF6MH/Traag_2015.pdf:application/pdf
  \endverb
  \field{eprinttype}{arxiv}
  \field{day}{04}
  \field{month}{03}
  \field{year}{2015}
  \field{urlday}{27}
  \field{urlmonth}{05}
  \field{urlyear}{2015}
\endentry

\entry{Tumminello2005}{article}{}
  \name{author}{4}{}{%
    {{}%
     {Tumminello}{T.}%
     {M.}{M.}%
     {}{}%
     {}{}}%
    {{}%
     {Aste}{A.}%
     {T.}{T.}%
     {}{}%
     {}{}}%
    {{}%
     {Matteo}{M.}%
     {T.~Di}{T.~D.}%
     {}{}%
     {}{}}%
    {{}%
     {Mantegna}{M.}%
     {R.~N.}{R.~N.}%
     {}{}%
     {}{}}%
  }
  \keyw{cluster analysis,Complex networks,correlation analysis}
  \strng{namehash}{TM+1}
  \strng{fullhash}{TMATMTDMRN1}
  \field{abstract}{%
  We introduce a technique to filter out complex data sets by extracting a
  subgraph of representative links. Such a filtering can be tuned up to any
  desired level by controlling the genus of the resulting graph. We show that
  this technique is especially suitable for correlation-based graphs, giving
  filtered graphs that preserve the hierarchical organization of the minimum
  spanning tree but containing a larger amount of information in their internal
  structure. In particular in the case of planar filtered graphs (genus equal
  to 0), triangular loops and four-element cliques are formed. The application
  of this filtering procedure to 100 stocks in the U.S. equity markets shows
  that such loops and cliques have important and significant relationships with
  the market structure and properties.%
  }
  \verb{doi}
  \verb 10.1073/pnas.0500298102
  \endverb
  \verb{eprint}
  \verb 16027373
  \endverb
  \field{issn}{0027-8424, 1091-6490}
  \field{number}{30}
  \field{pages}{10421\bibrangedash 10426}
  \field{shortjournal}{PNAS}
  \field{title}{A tool for filtering information in complex systems}
  \verb{url}
  \verb http://www.pnas.org/content/102/30/10421
  \endverb
  \field{volume}{102}
  \field{langid}{english}
  \verb{file}
  \verb Tumminello et al_2005.pdf:/Users/jeff/Library/Application Support/Zoter
  \verb o/Profiles/eaysq4eo.default/zotero/storage/8VMFVNCI/Tumminello et al_20
  \verb 05.pdf:application/pdf;Snapshot:/Users/jeff/Library/Application Support
  \verb /Zotero/Profiles/eaysq4eo.default/zotero/storage/SAKNRHN2/10421.html:
  \endverb
  \field{journaltitle}{Proceedings of the National Academy of Sciences of the
  United States of America}
  \field{eprinttype}{pmid}
  \field{day}{26}
  \field{month}{07}
  \field{year}{2005}
  \field{urlday}{13}
  \field{urlmonth}{08}
  \field{urlyear}{2015}
\endentry

\entry{Gobbi2015}{software}{}
  \name{author}{2}{}{%
    {{}%
     {Gobbi}{G.}%
     {Andrea}{A.}%
     {}{}%
     {}{}}%
    {{}%
     {Iorio}{I.}%
     {Francesco}{F.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{GAIF1}
  \strng{fullhash}{GAIF1}
  \field{title}{{{BiRewire}}}
  \field{version}{2.2}
  \field{year}{2015}
\endentry

\entry{Hong2013}{article}{}
  \name{author}{1}{}{%
    {{}%
     {Hong}{H.}%
     {Yili}{Y.}%
     {}{}%
     {}{}}%
  }
  \keyw{Characteristic function,k -out-of- n system,Longevity risk,Normal
  approximation,Sum of independent random indicators,Warranty returns}
  \strng{namehash}{HY1}
  \strng{fullhash}{HY1}
  \field{abstract}{%
  The Poisson binomial distribution is the distribution of the sum of
  independent and non-identically distributed random indicators. Each indicator
  follows a Bernoulli distribution and the individual probabilities of success
  vary. When all success probabilities are equal, the Poisson binomial
  distribution is a binomial distribution. The Poisson binomial distribution
  has many applications in different areas such as reliability, actuarial
  science, survey sampling, econometrics, etc. The computing of the cumulative
  distribution function (cdf) of the Poisson binomial distribution, however, is
  not straightforward. Approximation methods such as the Poisson approximation
  and normal approximations have been used in literature. Recursive formulae
  also have been used to compute the cdf in some areas. In this paper, we
  present a simple derivation for an exact formula with a closed-form
  expression for the cdf of the Poisson binomial distribution. The derivation
  uses the discrete Fourier transform of the characteristic function of the
  distribution. We develop an algorithm that efficiently implements the exact
  formula. Numerical studies were conducted to study the accuracy of the
  developed algorithm and approximation methods. We also studied the
  computational efficiency of different methods. The paper is concluded with a
  discussion on the use of different methods in practice and some suggestions
  for practitioners.%
  }
  \verb{doi}
  \verb 10.1016/j.csda.2012.10.006
  \endverb
  \field{issn}{0167-9473}
  \field{pages}{41\bibrangedash 51}
  \field{shortjournal}{Computational Statistics \& Data Analysis}
  \field{title}{On computing the distribution function for the {{Poisson}}
  binomial distribution}
  \verb{url}
  \verb http://www.sciencedirect.com/science/article/pii/S0167947312003568
  \endverb
  \field{volume}{59}
  \verb{file}
  \verb ScienceDirect Full Text PDF:/Users/jeff/Library/Application Support/Zot
  \verb ero/Profiles/eaysq4eo.default/zotero/storage/2R4PN79T/Hong - 2013 - On
  \verb computing the distribution function for the Poi.pdf:application/pdf;Sci
  \verb enceDirect Snapshot:/Users/jeff/Library/Application Support/Zotero/Prof
  \verb iles/eaysq4eo.default/zotero/storage/CI59QTZX/S0167947312003568.html:
  \endverb
  \field{journaltitle}{Computational Statistics \& Data Analysis}
  \field{month}{03}
  \field{year}{2013}
  \field{urlday}{14}
  \field{urlmonth}{07}
  \field{urlyear}{2015}
\endentry

\entry{Fernandez2010}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Fernandez}{F.}%
     {M.}{M.}%
     {}{}%
     {}{}}%
    {{}%
     {Williams}{W.}%
     {S.}{S.}%
     {}{}%
     {}{}}%
  }
  \keyw{binomial coefficient,binomial cumulative distribution function,binomial
  distribution,closed-form expression,Closed-form solution,computing
  speed,Distributed computing,Distribution functions,Fault tolerant
  systems,Poisson-binomial,Poisson distribution,probability,probability density
  function,Resource management,Signal detection,simplifying analysis,Target
  tracking,Testing,trigonometric identities,Workstations}
  \strng{namehash}{FMWS1}
  \strng{fullhash}{FMWS1}
  \field{abstract}{%
  The Poisson-binomial probability density function (pdf) describes the numbers
  of successes in N independent trials, when the individual probabilities of
  success vary across trials. Its use is pervasive in applications, such as
  fault tolerance, signal detection, target tracking, object
  classification/identification, multi-sensor data fusion, system management,
  and performance characterization, among others. We present a closed-form
  expression for this pdf, and we discuss several of its advantages regarding
  computing speed and implementation and in simplifying analysis, with examples
  of the latter including the computation of moments and the development of new
  trigonometric identities for the binomial coefficient and the binomial
  cumulative distribution function (cdf). Finally we also pose and address the
  inverse Poisson-binomial problem; that is, given such pdf, how to find
  (within a permutation) the probabilities of success of the individual
  trials.%
  }
  \verb{doi}
  \verb 10.1109/TAES.2010.5461658
  \endverb
  \field{issn}{0018-9251}
  \field{number}{2}
  \field{pages}{803\bibrangedash 817}
  \field{title}{Closed-{{Form Expression}} for the {{Poisson-Binomial
  Probability Density Function}}}
  \field{volume}{46}
  \verb{file}
  \verb IEEE Xplore Full Text PDF:/Users/jeff/Library/Application Support/Zoter
  \verb o/Profiles/eaysq4eo.default/zotero/storage/53ZS6SGX/Fernandez and Willi
  \verb ams - 2010 - Closed-Form Expression for the Poisson-Binomial Pr.pdf:app
  \verb lication/pdf;IEEE Xplore Abstract Record:/Users/jeff/Library/Applicatio
  \verb n Support/Zotero/Profiles/eaysq4eo.default/zotero/storage/B525QDSR/abs_
  \verb all.html:
  \endverb
  \field{journaltitle}{IEEE Transactions on Aerospace and Electronic Systems}
  \field{month}{04}
  \field{year}{2010}
\endentry

\entry{Chen1997}{article}{}
  \name{author}{2}{}{%
    {{}%
     {Chen}{C.}%
     {Sean~X.}{S.~X.}%
     {}{}%
     {}{}}%
    {{}%
     {Liu}{L.}%
     {Jun~S.}{J.~S.}%
     {}{}%
     {}{}}%
  }
  \strng{namehash}{CSXLJS1}
  \strng{fullhash}{CSXLJS1}
  \field{number}{4}
  \field{pages}{875\bibrangedash 892}
  \field{title}{Statistical applications of the {{Poisson}}-binomial and
  conditional {{Bernoulli}} distributions}
  \verb{url}
  \verb http://www3.stat.sinica.edu.tw/statistica/password.asp?vol=7&num=4&art=
  \verb 4
  \endverb
  \field{volume}{7}
  \verb{file}
  \verb [PDF] from sinica.edu.tw:/Users/jeff/Library/Application Support/Zotero
  \verb /Profiles/eaysq4eo.default/zotero/storage/M2CGG3RG/Chen and Liu - 1997
  \verb - Statistical applications of the Poisson-binomial a.pdf:application/pd
  \verb f
  \endverb
  \field{journaltitle}{Statistica Sinica}
  \field{year}{1997}
  \field{urlday}{21}
  \field{urlmonth}{07}
  \field{urlyear}{2015}
\endentry

\lossort
\endlossort

\endinput
