Probabilistic models for discovering e-communities

Ding Zhou, Eren Manavoglu, Jia Li, C. Lee Giles, Hongyuan Zha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

177 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 15th International Conference on World Wide Web
Pages173-182
Number of pages10
DOIs
StatePublished - 2006
Event15th International Conference on World Wide Web - Edinburgh, Scotland, United Kingdom
Duration: May 23 2006May 26 2006

Publication series

NameProceedings of the 15th International Conference on World Wide Web

Other

Other15th International Conference on World Wide Web
Country/TerritoryUnited Kingdom
CityEdinburgh, Scotland
Period5/23/065/26/06

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

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