%0 Conference Paper %1 citeulike:9578595 %A Zhang, Haizheng %A Qiu, Baojun %A Giles, C. L. %A Foley, H. C. %A Yen, J. %B Intelligence and Security Informatics, 2007 IEEE %D 2007 %I IEEE %K dimensionality-reduction social-network %P 200--207 %R 10.1109/isi.2007.379553 %T An LDA-based Community Structure Discovery Approach for Large-Scale Social Networks %U http://dx.doi.org/10.1109/isi.2007.379553 %X Community discovery has drawn significant research interests among researchers from many disciplines for its increasing application in multiple, disparate areas, including computer science, biology, social science and so on. This paper describes an LDA(latent Dirichlet Allocation)-based hierarchical Bayesian algorithm, namely SSN-LDA (simple social network LDA). In SSN-LDA, communities are modeled as latent variables in the graphical model and defined as distributions over the social actor space. The advantage of SSN-LDA is that it only requires topological information as input. This model is evaluated on two research collaborative networkst: CtteSeer and NanoSCI. The experimental results demonstrate that this approach is promising for discovering community structures in large-scale networks.

@inproceedings{citeulike:9578595, abstract = {{Community discovery has drawn significant research interests among researchers from many disciplines for its increasing application in multiple, disparate areas, including computer science, biology, social science and so on. This paper describes an LDA(latent Dirichlet Allocation)-based hierarchical Bayesian algorithm, namely SSN-LDA (simple social network LDA). In SSN-LDA, communities are modeled as latent variables in the graphical model and defined as distributions over the social actor space. The advantage of SSN-LDA is that it only requires topological information as input. This model is evaluated on two research collaborative networkst: CtteSeer and NanoSCI. The experimental results demonstrate that this approach is promising for discovering community structures in large-scale networks.}}, added-at = {2018-03-19T12:24:51.000+0100}, author = {Zhang, Haizheng and Qiu, Baojun and Giles, C. L. and Foley, H. C. and Yen, J.}, biburl = {https://www.bibsonomy.org/bibtex/28985a1b78de441efa1e203f218b1159f/aho}, booktitle = {Intelligence and Security Informatics, 2007 IEEE}, citeulike-article-id = {9578595}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/isi.2007.379553}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4258697}, doi = {10.1109/isi.2007.379553}, institution = {Pennsylvania State Univ, University Park}, interhash = {eef32aa75abab2873ffa077ed0648280}, intrahash = {8985a1b78de441efa1e203f218b1159f}, keywords = {dimensionality-reduction social-network}, month = may, pages = {200--207}, posted-at = {2011-12-15 17:36:39}, priority = {2}, publisher = {IEEE}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{An LDA-based Community Structure Discovery Approach for Large-Scale Social Networks}}, url = {http://dx.doi.org/10.1109/isi.2007.379553}, year = 2007 }