Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

Zhiqiang Xu, James Cheng, Xiaokui Xiao, Ryohei Fujimaki, Yusuke Muraoka

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.
Original languageEnglish (US)
Pages (from-to)239-268
Number of pages30
JournalKnowledge and Information Systems
Volume53
Issue number1
DOIs
StatePublished - Feb 16 2017

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