Overlapping community detection in weighted networks via a Bayesian approach

Yi Chen, Xiaolong Wang, Xin Xiang, Buzhou Tang, Qingcai Chen, Shixi Fan and Junzhao Bu

Physica A: Statistical Mechanics and its Applications, 2017, vol. 468, issue C, 790-801

Abstract: Complex networks as a powerful way to represent complex systems have been widely studied during the past several years. One of the most important tasks of complex network analysis is to detect communities embedded in networks. In the real world, weighted networks are very common and may contain overlapping communities where a node is allowed to belong to multiple communities. In this paper, we propose a novel Bayesian approach, called the Bayesian mixture network (BMN) model, to detect overlapping communities in weighted networks. The advantages of our method are (i) providing soft-partition solutions in weighted networks; (ii) providing soft memberships, which quantify ‘how strongly’ a node belongs to a community. Experiments on a large number of real and synthetic networks show that our model has the ability in detecting overlapping communities in weighted networks and is competitive with other state-of-the-art models at shedding light on community partition.

Keywords: Overlapping community; Community detection; Weighted networks; Bayesian approach (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:468:y:2017:i:c:p:790-801

DOI: 10.1016/j.physa.2016.11.098

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