Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering
dc.contributor.author | Xu, Zhiqiang | |
dc.contributor.author | Cheng, James | |
dc.contributor.author | Xiao, Xiaokui | |
dc.contributor.author | Fujimaki, Ryohei | |
dc.contributor.author | Muraoka, Yusuke | |
dc.date.accessioned | 2017-02-28T11:32:08Z | |
dc.date.available | 2017-02-28T11:32:08Z | |
dc.date.issued | 2017-02-16 | |
dc.identifier.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. | |
dc.identifier.issn | 0219-1377 | |
dc.identifier.issn | 0219-3116 | |
dc.identifier.doi | 10.1007/s10115-017-1030-8 | |
dc.identifier.uri | http://hdl.handle.net/10754/622931 | |
dc.description.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. | |
dc.description.sponsorship | 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. | |
dc.publisher | Springer Nature | |
dc.relation.url | http://link.springer.com/article/10.1007/s10115-017-1030-8 | |
dc.rights | The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-017-1030-8 | |
dc.subject | Attributed graph clustering | |
dc.subject | Model selection | |
dc.subject | Dirichlet process | |
dc.subject | Factorized information criterion | |
dc.title | Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering | |
dc.type | Article | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.identifier.journal | Knowledge and Information Systems | |
dc.eprint.version | Post-print | |
dc.contributor.institution | Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, China | |
dc.contributor.institution | School of Computer Engineering, Nanyang Technological University, Singapore, Singapore | |
dc.contributor.institution | NEC Laboratories America, Cupertino, USA | |
dc.contributor.institution | NEC Laboratories Japan, Nakahara-ku, Kawasaki-shi, Japan | |
kaust.person | Xu, Zhiqiang | |
refterms.dateFOA | 2018-02-16T00:00:00Z | |
dc.date.published-online | 2017-02-16 | |
dc.date.published-print | 2017-10 |
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