Probabilistic Models for Discovering E-Communities

• Ding Zhou, Department of Computer Science and Engineering, The Pennsylvania State University, USA
• Eren Manavoglu, Department of Computer Science and Engineering, The Pennsylvania State University, USA
• Jia Li, Department of Statistics The Pennsylvania State University AND Department of Computer Science and Engineering, The Pennsylvania State University, USA
• C. Lee Giles, Pennsylvania State University, USA
• Hongyuan Zha, Department of Computer Science and Engineering The Pennsylvania State University AND Department of Statistics The Pennsylvania State University, USA

Full text:

Track: E* Applications: E-Communities, E-Learning, E-Commerce, E-Science, E-Government, and E-Humanities

Slot: 16:00-17:30, Wednesday 24th May

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.

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