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.
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Jiang, Q., Zhang, Y., Sun, M. (2009). Community Detection on Weighted Networks: A Variational Bayesian Method. In: Zhou, ZH., Washio, T. (eds) Advances in Machine Learning. ACML 2009. Lecture Notes in Computer Science(), vol 5828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05224-8_15
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DOI: https://doi.org/10.1007/978-3-642-05224-8_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05223-1
Online ISBN: 978-3-642-05224-8
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