%0 Conference Paper %1 Zhou06modelsCommunities %A Zhou, Ding %A Manavoglu, Eren %A Li, Jia %A Giles, C. Lee %A Zha, Hongyuan %B WWW '06: Proceedings of the 15th international conference on World Wide Web %C New York, NY, USA %D 2006 %I ACM %K Zhou06modelsCommunities community modelling toread %P 173--182 %R http://doi.acm.org/10.1145/1135777.1135807 %T Probabilistic models for discovering e-communities %U http://portal.acm.org/citation.cfm?id=1135807&dl=GUIDE&coll=GUIDE&CFID=64167610&CFTOKEN=59359426 %X 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. %@ 1-59593-323-9

@inproceedings{Zhou06modelsCommunities, 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.}, added-at = {2008-11-12T20:27:47.000+0100}, address = {New York, NY, USA}, author = {Zhou, Ding and Manavoglu, Eren and Li, Jia and Giles, C. Lee and Zha, Hongyuan}, biburl = {https://www.bibsonomy.org/bibtex/2846c8f476023f711938bf299b858c16b/lee_peck}, booktitle = {WWW '06: Proceedings of the 15th international conference on World Wide Web}, description = {Probabilistic models for discovering e-communities}, doi = {http://doi.acm.org/10.1145/1135777.1135807}, interhash = {41dc4403ba06a0f6b7ac9dc988540721}, intrahash = {846c8f476023f711938bf299b858c16b}, isbn = {1-59593-323-9}, keywords = {Zhou06modelsCommunities community modelling toread}, location = {Edinburgh, Scotland}, pages = {173--182}, publisher = {ACM}, timestamp = {2008-11-12T20:27:47.000+0100}, title = {Probabilistic models for discovering e-communities}, url = {http://portal.acm.org/citation.cfm?id=1135807&dl=GUIDE&coll=GUIDE&CFID=64167610&CFTOKEN=59359426}, year = 2006 }