Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Probabilistic models for discovering e-communities
Proceedings of the 15th international conference on World Wide Web
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expander flows, geometric embeddings and graph partitioning
Journal of the ACM (JACM)
TeleComVis: Exploring Temporal Communities in Telecom Networks
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Detecting communities plays a great important role in sociology, biology and computer science, disciplines where systems are often modeled as graphs. Such inherent community structures make us deeply understand about the networks and therefore have drawn significant interests among researchers. This paper describes a probabilistic community detection algorithm by modeling topic on sampling sub graphs. In this algorithm, the communities are modeled as latent topic variables of an LDA topic model and the vertices of sampling sub graphs are drawn from these topics with different probabilities. This paper also proposes a sub graph sampling algorithm and explores its impact on community detection performance. Our algorithm is evaluated by extensive experiments using many computer-generated artificial graphs and real-world networks. The results show that our algorithm is effective in detecting probabilistic community.