Tracking communities in dynamic social networks

Authors:
Kevin S. Xu;Mark Kliger;Alfred O. Hero, III
Affiliations:
EECS Department, University of Michigan, Ann Arbor, MI;Medasense Biometrics Ltd., Ofakim, Israel;EECS Department, University of Michigan, Ann Arbor, MI
Venue:
SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
Year:
2011

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Abstract

The study of communities in social networks has attracted considerable interest from many disciplines. Most studies have focused on static networks, and in doing so, have neglected the temporal dynamics of the networks and communities. This paper considers the problem of tracking communities over time in dynamic social networks. We propose a method for community tracking using an adaptive evolutionary clustering framework. We apply the method to reveal the temporal evolution of communities in two real data sets. In addition, we obtain a statistic that can be used for identifying change points in the network.