Community Detection on Weighted Networks: A Variational Bayesian Method

  • Qixia Jiang
  • Yan Zhang
  • Maosong Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5828)


Massive real-world data are network-structured, such as social network, relationship between proteins and power grid. Discovering the latent communities is a useful way for better understanding the property of a network. In this paper, we present a fast, effective and robust method for community detection. We extend the constrained Stochastic Block Model (conSBM) on weighted networks and use a Bayesian method for both parameter estimation and community number identification. We show how our method utilizes the weight information within the weighted networks, reduces the computation complexity to handle large-scale weighted networks, measure the estimation confidence and automatically identify the community number. We develop a variational Bayesian method for inference and parameter estimation. We demonstrate our method on a synthetic data and three real-world networks. The results illustrate that our method is more effective, robust and much faster.


Bayesian Information Criterion Latent Dirichlet Allocation Community Detection Weighted Network Collaboration Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Qixia Jiang
    • 1
  • Yan Zhang
    • 1
  • Maosong Sun
    • 1
  1. 1.State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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