Graphical Models Based Hierarchical Probabilistic Community Discovery in Large-scale Social Networks
Document Type
Article
Department or Administrative Unit
Finance and Supply Chain Management
Publication Date
2010
Abstract
Real-world social networks, while disparate in nature, often comprise of a set of loose clusters (a.k.a. communities), in which members are better connected to each other than to the rest of the network. In addition, such communities are often hierarchical, reflecting the fact that some communities are composed of a few smaller, sub-communities. Discovering the complicated hierarchical community structure can gain us deeper understanding about the networks and the pertaining communities. This paper describes a hierarchical Bayesian model based scheme namely hierarchical social network-pachinko allocation model (HSN-PAM), for discovering probabilistic, hierarchical communities in social networks. This scheme is powered by a previously developed hierarchical Bayesian model. In this scheme, communities are classified into two categories: super-communities and regular-communities. Two different network encoding approaches are explored to evaluate this scheme on research collaborative networks, including CiteSeer. The experimental results demonstrate that HSN-PAM is effective for discovering hierarchical community structures in large-scale social networks.
Recommended Citation
Zhang, H., Ke, K., Li, W. & Wang, X. (2010). Graphical models based hierarchical probabilistic community discovery in large-scale social networks. International Journal of Data Mining, Modeling and Management 2(2), 95-116. DOI: 10.1504/IJDMMM.2010.032144
Journal
International Journal of Data Mining, Modeling and Management
Rights
Copyright © 2010 Inderscience Enterprises Ltd.
Comments
This article was originally published in International Journal of Data Mining, Modeling and Management. The full-text article from the publisher can be found here.
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