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
Finding experts and their eetails in e-mail corpora
Proceedings of the 15th international conference on World Wide Web
Mining blog stories using community-based and temporal clustering
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Topic-link LDA: joint models of topic and author community
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Structured correspondence topic models for mining captioned figures in biological literature
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Connections between the lines: augmenting social networks with text
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding a team of experts in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Co-evolution of social and affiliation networks
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
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Personalized Travel Package Recommendation
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
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Discovering communities from social media and collaboration systems has been of great interest in recent years. Existing work show prospects of modeling contents and social links, aiming at discovering social communities, whose definition varies by application. We believe that a community depends not only on the group of people who actively participate, but also the topics they communicate about or collaborate on. This is especially true for workplace email communications. Within an organization, it is not uncommon that employees multifunction, and groups of employees collaborate on multiple projects at the same time. In this paper, we aim to automatically discovering and profiling users' communities by taking into account both the contacts and the topics. More specifically, we propose a community profiling model called COCOMP, where the communities labels are latent, and each social document corresponds to an information sharing activity among the most probable community members regarding the most relevant community issues. Experiment results on several social communication datasets, including emails and Twitter messages, demonstrate that the model can discover users' communities effectively, and provide concrete semantics.