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

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    Type
    Article
    Authors
    Xu, Zhiqiang
    Cheng, James
    Xiao, Xiaokui
    Fujimaki, Ryohei
    Muraoka, Yusuke
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2017-02-16
    Online Publication Date
    2017-02-16
    Print Publication Date
    2017-10
    Permanent link to this record
    http://hdl.handle.net/10754/622931
    
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    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.
    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 Nature
    Journal
    Knowledge and Information Systems
    DOI
    10.1007/s10115-017-1030-8
    Additional Links
    http://link.springer.com/article/10.1007/s10115-017-1030-8
    ae974a485f413a2113503eed53cd6c53
    10.1007/s10115-017-1030-8
    Scopus Count
    Collections
    Articles; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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