Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering
Online Publication Date2017-02-16
Print Publication Date2017-10
Permanent link to this recordhttp://hdl.handle.net/10754/622931
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AbstractAttributed 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.
CitationXu Z, Cheng J, Xiao X, Fujimaki R, Muraoka Y (2017) Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering. Knowledge and Information Systems. Available: http://dx.doi.org/10.1007/s10115-017-1030-8.
SponsorsThe authors would like to thank the anonymous reviewers of the paper for their valuable comments that help significantly improve the quality of the paper.