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 EnFGibbs 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. Categories and Subject Descriptors H.4 [Information Systems Applications]: Miscellaneous; G.3 [Probability and Statistics]: Models; J.4 [Social and...

• Type: Conference
• Year: 2006
• Where: WWW
• Authors: Ding Zhou, Eren Manavoglu, Jia Li, C. Lee Giles, Hongyuan Zha
• Keywords: Generative Bayesian Models, Internet Technology, Semantic Community Discovery, Social Network, WWW 2006