Collaboration over time: Characterizing and modeling network evolution

A formal type of scientific and academic collaboration is coauthorship which can be represented by a coauthorship network. Coauthorship networks are among some of the largest social networks and offer us the opportunity to study the mechanisms underlying large-scale real world networks. We construct such a network for the Computer Science field covering research collaborations from 1980 to 2005, based on a large dataset of 451,305 papers authored by 283,174 distinct researchers. By mining this network, we first present a comprehensive study of the network statistical properties for a longitudinal network at the overall network level as well as for the intermediate community level. Major observations are that the database community is the best connected while the AI community is the most assortative, and that the Computer Science field as a whole shows a collaboration pattern more similar to Mathematics than to Biology. Moreover, the small world phenomenon and the scale-free degree distribution accompany the growth of the network. To study the individual collaborations, we propose a novel stochastic model, Stochastic Poisson model with Optimization Tree (Spot)to efficiently predict any increment of collaboration based on the local neighborhood structure. Spot models the non-stationary Poisson process by maximizing the log-likelihood with a tree structure. Empirical results show that Spot outperforms Support Vector Regression by better fitting collaboration records and predicting the rate of collaboration.