<p>To elaborate a little on my comment (which is slightly tangential).</p>

<p>There is a fun site called <a href="http://www.eigenfactor.org" rel="nofollow">eigenfactor</a> that provides nice visualizations of how various parts of science cite each other, and their relative size by publication/citation volume. Unfortunately their primary focus seems to be calculating impact factors for journals and so they operate on the level of journals, not individual papers/authors. However, they have a pretty interesting way of identifying subfields.</p>

<p>They use the number of citations from a journal A to a journal B as a weight on the edge from A to B. They then consider a random walk on the resulting graph. Such a walk tends to linger in topic clusters, and thus when they try to create a shortest description of the walk they tend to name the clusters and use a second index inside the cluster (kind of how you would have unique city names, and unique street names in each city, but redundant across cities). I thought it was a pretty nice way of finding clusters, and produces clusters that mimic how most people would divide the topics in science (with the occasional hilarious classification like putting Phys. Rev. Letters in Chemistry instead of Physics) You can read more in their <a href="http://octavia.zoology.washington.edu/publications/RosvallAndBergstrom08.pdf" rel="nofollow">paper</a> or on the <a href="http://www.mapequation.org" rel="nofollow">mapequation</a> site.</p>

CC BY-SA 2.5
2011-01-22T20:53:19.840Z
Nice thing to play with it, thank you. Would be great to have the same thing for papers!
Tatiana Starikovskaya
CC BY-SA 2.5
2011-01-22T19:01:40.833Z
2011-01-22T19:01:40.833Z
Artem Kaznatcheev
<p>This is actually a non-trivial problem and active area of research.</p>

<p>Plugging citeseer data into a graph visualizer is very doable (ex. Pajek), and will give you a decent way to browse if you size things by PageRank and maybe include a cutoff threshold (though missing obscure papers that would be useful to you is still an issue). Unfortunately, knowing why one paper cites another is often difficult. Computer science papers often have huge lists of citations, most of which are not vital to intellectual comprehension of a particular paper. Whether papers are being cited because they represent a classic result, are being improved upon, are being contrasted with, or actually helped create the ideas of the current paper can't be known without some textual analysis (though other methods might offer some help).</p>

<p>Surfing through a visualizer for "prominent papers" would be more aesthetically pleasant but not much more useful than surfing google scholar; mega-papers with hundreds of citations, but not necessarily related to the subfield you desire, would dominate the landscape. Also, certain interdisciplinary papers could "pollute" otherwise neat topic partitions.</p>

<p>I'm actually exaggerating the problem some. Using a visualizer on citation data the way you described, if well-implemented (mainly trimming the results so they're not too cluttered but so that it isn't reduced to only big-name results), could be very very helpful, especially with awareness of its limitations. All I'm really saying is that a good survey paper is probably the best possible option to examine a subfield, if one exists.</p>

CC BY-SA 2.5
2011-01-28T08:52:11.510Z
Thank for you answer! Actually, after posting a question I have found this link http://www.cs.umd.edu/hcil/InfovisRepository/contest-2004/17/unzip/entry.html, where the authors do exactly what I want, but only for a small set of data.
Tatiana Starikovskaya
CC BY-SA 2.5
2011-01-28T20:58:57.863Z
For a good visualization that can handle a large amount of data and not be an eyesore (in contrast to the otherwise nifty link you give), you probably want the following things: 1) nodes sized proportional to some metric (e.g. pageRank) 2) zoom feature that hides small/less relevant nodes at a distance 3) hierarchical clustering that shows you high-level clusters at long zoom (with a best-guess label) and low-level clusters at close zoom. I wish I could point you to some software that would do this, but I don't know if there is or isn't.
Elliot JJ
CC BY-SA 2.5
2011-02-16T09:22:55.727Z
Think Tulip can http://tulip.labri.fr/. Sorry not to answer earlier.
Tatiana Starikovskaya
CC BY-SA 2.5
2011-01-27T06:16:14.610Z
2011-01-27T06:16:14.610Z
Elliot JJ
<p>I'm looking for a software for visualizing main trends in some field. 
Specifically, I want it to build a graph of the field with vertexes representing papers and edges representing how close papers are (probably, based on number of co-citations).
It seems to be very useful in order to find important subfields (clusters in the graph) and important papers (centers of the clusters).</p>

CC BY-SA 2.5
2011-01-22T12:50:04.997Z
Have you seen [this post](http://cstheory.stackexchange.com/questions/2573/how-to-know-if-x-and-y-have-coauthored/2583#2583)?
Sadeq Dousti
CC BY-SA 2.5
2011-01-22T13:19:28.037Z
I haven't, thanks. I have just tried both academic search by microsoft and dblp visualiser, but they do not show specific papers, unfortunately.
Tatiana Starikovskaya
CC BY-SA 2.5
2011-01-22T13:23:19.380Z
I can see the resulting web application that allows you to browse your field like that. Good question! I don't know a tool, but you might find something related to semantic web.
Raphael
CC BY-SA 2.5
2011-01-22T16:53:19.633Z
Not exactly what you are looking for (since it operates on the level of journals, not individual papers) but http://www.eigenfactor.org/ provides some great visualizations of how different fields of science relate and are clustered. The team behind it also have a few good papers on how to identify clusters or sub-fields relatively robustly: http://www.eigenfactor.org/papers.htm
Artem Kaznatcheev
CC BY-SA 2.5
2011-01-22T17:20:28.173Z
Sadeq, Artem: your comments might very well be answers.
Suresh Venkat
CC BY-SA 2.5
2011-01-22T19:38:56.527Z
I guess using [DBLP data file](http://dbis.uni-trier.de/DBL-Browser/downloads/dblpCOMP.zip) it should not be difficult to implement one (no, I am not going to do it). Someone can give it to one of their students as a useful project which has some nice algorithmic aspects.
Kaveh
<soft-question><advice-request><research-practice><software><bibliography>
A System for Visualizing Bibliography Information
Tatiana Starikovskaya
# A System for Visualizing Bibliography Information

(Asked by: username_0 on 2011-01-22)

I'm looking for a software for visualizing main trends in some field.
Specifically, I want it to build a graph of the field with vertexes representing papers and edges representing how close papers are (probably, based on number of co-citations).
It seems to be very useful in order to find important subfields (clusters in the graph) and important papers (centers of the clusters).

**Tags:** soft-question, advice-request, research-practice, software, bibliography
> **Comments:**
> **username_1**: Have you seen [this post](http://cstheory.stackexchange.com/questions/2573/how-to-know-if-x-and-y-have-coauthored/2583#2583)?  
> **username_0**: I haven't, thanks. I have just tried both academic search by microsoft and dblp visualiser, but they do not show specific papers, unfortunately.  
> **username_2**: I can see the resulting web application that allows you to browse your field like that. Good question! I don't know a tool, but you might find something related to semantic web.  
> **username_3**: Not exactly what you are looking for (since it operates on the level of journals, not individual papers) but http://www.eigenfactor.org/ provides some great visualizations of how different fields of science relate and are clustered. The team behind it also have a few good papers on how to identify clusters or sub-fields relatively robustly: http://www.eigenfactor.org/papers.htm  
> **username_4**: Sadeq, Artem: your comments might very well be answers.  
> **username_5**: I guess using [DBLP data file](http://dbis.uni-trier.de/DBL-Browser/downloads/dblpCOMP.zip) it should not be difficult to implement one (no, I am not going to do it). Someone can give it to one of their students as a useful project which has some nice algorithmic aspects.  

---

## 2 answers

### Answer by username_3 on 2011-01-22. Score: 3

To elaborate a little on my comment (which is slightly tangential).

There is a fun site called [eigenfactor](http://www.eigenfactor.org) that provides nice visualizations of how various parts of science cite each other, and their relative size by publication/citation volume. Unfortunately their primary focus seems to be calculating impact factors for journals and so they operate on the level of journals, not individual papers/authors. However, they have a pretty interesting way of identifying subfields.

They use the number of citations from a journal A to a journal B as a weight on the edge from A to B. They then consider a random walk on the resulting graph. Such a walk tends to linger in topic clusters, and thus when they try to create a shortest description of the walk they tend to name the clusters and use a second index inside the cluster (kind of how you would have unique city names, and unique street names in each city, but redundant across cities). I thought it was a pretty nice way of finding clusters, and produces clusters that mimic how most people would divide the topics in science (with the occasional hilarious classification like putting Phys. Rev. Letters in Chemistry instead of Physics) You can read more in their [paper](http://octavia.zoology.washington.edu/publications/RosvallAndBergstrom08.pdf) or on the [mapequation](http://www.mapequation.org) site.

> **Comments:**
> **username_0**: Nice thing to play with it, thank you. Would be great to have the same thing for papers!  

---

### Answer by username_6 on 2011-01-27. Score: 3

This is actually a non-trivial problem and active area of research.

Plugging citeseer data into a graph visualizer is very doable (ex. Pajek), and will give you a decent way to browse if you size things by PageRank and maybe include a cutoff threshold (though missing obscure papers that would be useful to you is still an issue). Unfortunately, knowing why one paper cites another is often difficult. Computer science papers often have huge lists of citations, most of which are not vital to intellectual comprehension of a particular paper. Whether papers are being cited because they represent a classic result, are being improved upon, are being contrasted with, or actually helped create the ideas of the current paper can't be known without some textual analysis (though other methods might offer some help).

Surfing through a visualizer for "prominent papers" would be more aesthetically pleasant but not much more useful than surfing google scholar; mega-papers with hundreds of citations, but not necessarily related to the subfield you desire, would dominate the landscape. Also, certain interdisciplinary papers could "pollute" otherwise neat topic partitions.

I'm actually exaggerating the problem some. Using a visualizer on citation data the way you described, if well-implemented (mainly trimming the results so they're not too cluttered but so that it isn't reduced to only big-name results), could be very very helpful, especially with awareness of its limitations. All I'm really saying is that a good survey paper is probably the best possible option to examine a subfield, if one exists.

> **Comments:**
> **username_0**: Thank for you answer! Actually, after posting a question I have found this link http://www.cs.umd.edu/hcil/InfovisRepository/contest-2004/17/unzip/entry.html, where the authors do exactly what I want, but only for a small set of data.  
> **username_6**: For a good visualization that can handle a large amount of data and not be an eyesore (in contrast to the otherwise nifty link you give), you probably want the following things: 1) nodes sized proportional to some metric (e.g. pageRank) 2) zoom feature that hides small/less relevant nodes at a distance 3) hierarchical clustering that shows you high-level clusters at long zoom (with a best-guess label) and low-level clusters at close zoom. I wish I could point you to some software that would do this, but I don't know if there is or isn't.  
> **username_0**: Think Tulip can http://tulip.labri.fr/. Sorry not to answer earlier.  

---

