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dc.contributor.authorPeng, Chengbin
dc.contributor.authorZhang, Zhihua
dc.contributor.authorWong, Ka-Chun
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorKeyes, David E.
dc.date.accessioned2018-01-15T06:35:08Z
dc.date.available2018-01-15T06:35:08Z
dc.date.issued2017-11-24
dc.identifier.citationChengbin Peng, Zhihua Zhang, Ka-Chun Wong, Xiangliang Zhang, David E. Keyes. A scalable community detection algorithm for large graphs using stochastic block models. IDA. IOS Press; 2017;21: 1463–1485. doi:10.3233/IDA-163156
dc.identifier.issn1088-467X
dc.identifier.issn1571-4128
dc.identifier.doi10.3233/IDA-163156
dc.identifier.urihttp://hdl.handle.net/10754/626776
dc.description.abstractCommunity detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era of
dc.description.sponsorshipResearch reported in this publication was supported by King Abdullah University of Science and Technology (KAUST).
dc.publisherIOS Press
dc.relation.urlhttps://content.iospress.com/articles/intelligent-data-analysis/ida163156
dc.relation.urlhttps://www.ijcai.org/Proceedings/15/Papers/296.pdf
dc.rightsArchived with thanks to Intelligent Data Analysis
dc.subjectStochastic block model
dc.subjectparallel computing
dc.subjectcommunity detection
dc.titleA scalable community detection algorithm for large graphs using stochastic block models
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentExtreme Computing Research Center
dc.identifier.journalIntelligent Data Analysis
dc.eprint.versionPost-print
dc.contributor.institutionNingbo Institute of Industrial Technology, Ningbo, Zhejiang, China
dc.contributor.institutionShanghai Jiao Tong University, Shanghai, China
dc.contributor.institutionCity University of Hong Kong, Hong Kong
kaust.personPeng, Chengbin
kaust.personZhang, Xiangliang
kaust.personKeyes, David E.
refterms.dateFOA2018-06-13T14:57:33Z


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