Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering
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Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering.pdf
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2017-02-16Online Publication Date
2017-02-16Print Publication Date
2017-10Permanent link to this record
http://hdl.handle.net/10754/622931
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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.Citation
Xu 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.Sponsors
The authors would like to thank the anonymous reviewers of the paper for their valuable comments that help significantly improve the quality of the paper.Publisher
Springer NatureAdditional Links
http://link.springer.com/article/10.1007/s10115-017-1030-8ae974a485f413a2113503eed53cd6c53
10.1007/s10115-017-1030-8