Probabilistic models for discovering e-communities
Proceedings of the 15th international conference on World Wide Web
Topic-link LDA: joint models of topic and author community
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Probabilistic community discovery using hierarchical latent Gaussian mixture model
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Using content and interactions for discovering communities in social networks
Proceedings of the 21st international conference on World Wide Web
iTop: interaction based topic centric community discovery on twitter
Proceedings of the 5th Ph.D. workshop on Information and knowledge
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Social graphs have received renewed interest as a research topic with the advent of social networking websites. These online networks provide a rich source of data to study user relationships and interaction patterns on a large scale. In this paper, we propose a generative Bayesian model for extracting latent communities from a social graph. We assume that community memberships depend on topics of interest between users and the link relationships between them in the social graph topology. In addition, we make use of the nature of interaction to gauge user interests. Our model allows communities to be related to multiple topics and each user in the graph can be a member of multiple communities. This gives an insight into user interests and topical distribution in communities. We show the effectiveness of our model using a real world data set and also compare our model with existing community discovery methods.