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dc.contributor.authorXu, Zhiqiang
dc.contributor.authorCheng, James
dc.contributor.authorXiao, Xiaokui
dc.contributor.authorFujimaki, Ryohei
dc.contributor.authorMuraoka, Yusuke
dc.date.accessioned2017-02-28T11:32:08Z
dc.date.available2017-02-28T11:32:08Z
dc.date.issued2017-02-16
dc.identifier.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.
dc.identifier.issn0219-1377
dc.identifier.issn0219-3116
dc.identifier.doi10.1007/s10115-017-1030-8
dc.identifier.urihttp://hdl.handle.net/10754/622931
dc.description.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.
dc.description.sponsorshipThe authors would like to thank the anonymous reviewers of the paper for their valuable comments that help significantly improve the quality of the paper.
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/article/10.1007/s10115-017-1030-8
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s10115-017-1030-8
dc.subjectAttributed graph clustering
dc.subjectModel selection
dc.subjectDirichlet process
dc.subjectFactorized information criterion
dc.titleEfficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalKnowledge and Information Systems
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, China
dc.contributor.institutionSchool of Computer Engineering, Nanyang Technological University, Singapore, Singapore
dc.contributor.institutionNEC Laboratories America, Cupertino, USA
dc.contributor.institutionNEC Laboratories Japan, Nakahara-ku, Kawasaki-shi, Japan
kaust.personXu, Zhiqiang
refterms.dateFOA2018-02-16T00:00:00Z
dc.date.published-online2017-02-16
dc.date.published-print2017-10


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