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ISI
2007
Springer

An LDA-based Community Structure Discovery Approach for Large-Scale Social Networks

8 years 10 months ago
An LDA-based Community Structure Discovery Approach for Large-Scale Social Networks
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 networks:CiteSeer and NanoSCI. The experimental results demonstrate that this approach is promising for discovering community structures in large-scale networks.1
Haizheng Zhang, Baojun Qiu, C. Lee Giles, Henry C.
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where ISI
Authors Haizheng Zhang, Baojun Qiu, C. Lee Giles, Henry C. Foley, John Yen
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