Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An event-based framework for characterizing the evolutionary behavior of interaction graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Reverse engineering an agent-based hidden Markov model for complex social systems
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
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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.