Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Probabilistic models for discovering e-communities
Proceedings of the 15th international conference on World Wide Web
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Node roles and community structure in networks
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Exploration of Link Structure and Community-Based Node Roles in Network Analysis
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Applying latent dirichlet allocation to group discovery in large graphs
Proceedings of the 2009 ACM symposium on Applied Computing
Segmentation and Automated Social Hierarchy Detection through Email Network Analysis
Advances in Web Mining and Web Usage Analysis
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Discovering community-oriented roles of nodes in a social network
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
A spatial LDA model for discovering regional communities
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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We present a new probabilistic approach to modeling social interactions, that seamlessly integrates community discovery and role assignment for a deeper understanding of connectivity patterns in social networks. The devised approach is an unsupervised learning technique based on a Bayesian hierarchical model of social interactions. This model specifies an intuitive generative process, in which pairs of nodes in a social network are associated with communities as well as roles in the context of the respective communities, before that a directed interaction is possibly established between them. According to the generative semantics of the proposed model, nodes are represented as probability distributions over communities, while communities are represented as probability distributions over roles. Such distributions are unknown parameters of the proposed model, that are estimated from social-network data through approximated posterior inference and parameter estimation. A comparative evaluation over real-world social networks reveals that our approach outperforms state-of-the-art competitors in terms of link prediction.