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
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
Probabilistic model for discovering topic based communities in social networks
Proceedings of the 20th ACM international conference on Information and knowledge management
iTop: interaction based topic centric community discovery on twitter
Proceedings of the 5th Ph.D. workshop on Information and knowledge
Time-aware topic recommendation based on micro-blogs
Proceedings of the 21st ACM international conference on Information and knowledge management
Cascade-based community detection
Proceedings of the sixth ACM international conference on Web search and data mining
Identifying same wavelength groups from twitter: a sentiment based approach
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Discovery and analysis of evolving topical social discussions on unstructured microblogs
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Estimating sharer reputation via social data calibration
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
The role of research leaders on the evolution of scientific communities
Proceedings of the 22nd international conference on World Wide Web companion
Organizational overlap on social networks and its applications
Proceedings of the 22nd international conference on World Wide Web
Community detection in content-sharing social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
FRec: a novel framework of recommending users and communities in social media
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Are there cultural differences in event driven information propagation over social media?
Proceedings of the 2nd international workshop on Socially-aware multimedia
Mining groups of common interest: discovering topical communities with network flows
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Proceedings of the 7th ACM international conference on Web search and data mining
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In recent years, social networking sites have not only enabled people to connect with each other using social links but have also allowed them to share, communicate and interact over diverse geographical regions. Social network provide a rich source of heterogeneous data which can be exploited to discover previously unknown relationships and interests among groups of people. In this paper, we address the problem of discovering topically meaningful communities from a social network. We assume that a persons' membership in a community is conditioned on its social relationship, the type of interaction and the information communicated with other members of that community. We propose generative models that can discover communities based on the discussed topics, interaction types and the social connections among people. In our models a person can belong to multiple communities and a community can participate in multiple topics. This allows us to discover both community interests and user interests based on the information and linked associations. We demonstrate the effectiveness of our model on two real word data sets and show that it performs better than existing community discovery models.