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. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Xu, K. S., Kliger, M., & Hero, A. O. (2011). Tracking communities in dynamic social networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6589 LNCS, pp. 219–226). https://doi.org/10.1007/978-3-642-19656-0_32
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