Identifying Long Lived Social Communities Using Structural Properties

We present a two step procedure to identify long lasting communities, or evolutions, in social networks. First, we use axiomatic foundations to `rigorously' establish shorter, strongly-connected evolutions. In the second step, we use heuristics to combine these shorter evolutions to form longer evolutions. We apply the procedure on data generated from two networks - the DBLP co-authorship database and Live Journal blog data. We visually validate our algorithms by examining the topic evolution of the associated documents. Our results demonstrate that our algorithms, based solely on structural properties of the data (who interacts with whom), are able to track thematic trends in the literature. We then use a machine learning framework to identify the structural features of the early stages of a community's evolution are most useful for predicting the lifetime of the community. We find that (in order) size, intensity and stability are the most important features.