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ACML
2009
Springer

Community Detection on Weighted Networks: A Variational Bayesian Method

3 years 11 months ago
Community Detection on Weighted Networks: A Variational Bayesian Method
Abstract. 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.
Qixia Jiang, Yan Zhang, Maosong Sun
Added 25 May 2010
Updated 25 May 2010
Type Conference
Year 2009
Where ACML
Authors Qixia Jiang, Yan Zhang, Maosong Sun
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