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
Group and topic discovery from relations and text
Proceedings of the 3rd international workshop on Link discovery
Joint latent topic models for text and citations
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining link and content for community detection: a discriminative approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Knowledge Discovery from Citation Networks
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
iTopicModel: Information Network-Integrated Topic Modeling
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
On community outliers and their efficient detection in information networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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Community and topic are two widely studied patterns in social network analysis. However, most existing studies either utilize textual content to improve the community detection or use link structure to guide topic modeling. Recently, some studies take both the link emphasized community and text emphasized topic into account, but community and topic are modeled by using the same latent variable. However, community and topic are different from each other in practical aspects. Therefore, it is more reasonable to model the community and topic by using different variables. To discover community, topic and their relations simultaneously, a mutual enhanced infinite generative model (MEI) is proposed. This model discriminates the community and topic from one another and relates them together via community-topic distributions. Community and topic can be detected simultaneously and can be enhanced mutually during learning process. To detect the appropriate number of communities and topics automatically, Hierarchical/Dirichlet Process Mixture model (H/DPM) is employed. Gibbs sampling based approach is adopted to learn the model parameters. Experiments are conducted on the co-author network extracted from DBLP where each author is associated with his/her published papers. Experimental results show that our proposed model outperforms several baseline models in terms of perplexity and link prediction performance.