Community detection algorithm combining stochastic block model and attribute data clustering

Shun Kataoka, Takuto Kobayashi, Muneki Yasuda, Kazuyuki Tanaka

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

We propose a new algorithm to detect the community structure in a network that utilizes both the network structure and vertex attribute data. Suppose we have the network structure together with the vertex attribute data, that is, the information assigned to each vertex associated with the community to which it belongs. The problem addressed this paper is the detection of the community structure from the information of both the network structure and the vertex attribute data. Our approach is based on the Bayesian approach that models the posterior probability distribution of the community labels. The detection of the community structure in our method is achieved by using belief propagation and an EM algorithm. We numerically verified the performance of our method using computer-generated networks and real-world networks.

Original languageEnglish
Article number114802
Journaljournal of the physical society of japan
Volume85
Issue number11
DOIs
Publication statusPublished - 2016 Nov 15

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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