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Probabilistic models for discovering e-communities (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Zha, Hongyuan Li, Jia Zhou, Ding Manavoglu, Eren Giles, C. Lee |
| Abstract | 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. |
| File Format | |
| Publisher Date | 2006-01-01 |
| Access Restriction | Open |
| Subject Keyword | Social Network Analysis Semantic Topic Description Enf-gibbs Sampling Algorithm Enron Email Corpus Show Probabilistic Modeling Performance Problem Experimental Study Semantic Information Communication Frequency Computational Research Social Network Semantic Community Discovery Instant Messaging Traditional Method Community Detection Generative Bayesian Model Generative Model Probabilistic Model |
| Content Type | Text |
| Resource Type | Article |
