document: Probabilistic models for discovering e-communities The increasing amount of communication between individuals in e-formats ( e.g. email , Instant messaging and the Web ) has motivated computational research in social network analysis ( SNA ) . Previous work in SNA has emphasized the social network ( SN ) topology measured by communication frequencies while ignoring the semantic information in SNs . In this paper , we propose two generative Bayesian models for semantic community discovery in SNs , combining probabilistic modeling with community detection in SNs . To simulate the generative models , an EnF-Gibbs sampling algorithm is proposed to address the efficiency and performance problems of traditional methods . Experimental studies on Enron email corpus show that our approach successfully detects the communities of individuals and in addition provides semantic topic descriptions of these communities .
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