  Collaboration networks provide a method for examining the highly
heterogeneous structure of collaborative communities. However, we still have
limited theoretical understanding of how individual heterogeneity relates to
network heterogeneity. The model presented here provides a framework linking an
individual's skill set to her position in the collaboration network, and the
distribution of skills in the population to the structure of the collaboration
network as a whole. This model suggests that there is a non-trivial
relationship between skills and network position: individuals with a useful
combination of skills will have a disproportionate number of links in the
network. Indeed, in some cases, an individual's degree is non-monotonic in the
number of skills she has--an individual with very few skills may outperform an
individual with many. Special cases of the model suggest that the degree
distribution of the network will be skewed, even when the distribution of
skills is uniform in the population. The degree distribution becomes more
skewed as problems become more difficult, leading to a community dominated by a
few high-degree superstars. This has striking implications for labor market
outcomes in industries where production is largely the result of collaborative
effort.
