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.

• Type: Conference
• Year: 2009
• Where: ACML
• Authors: Qixia Jiang, Yan Zhang, Maosong Sun
• Keywords: ACML 2009, Bayesian Method, Machine Learning, Parameter Estimation, Weighted Networks