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Scalable and accurate graph clustering and community structure detection

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dc.contributor.author Djidjev, Hristo N.
dc.contributor.author Onuş, Melih
dc.date.accessioned 2020-05-15T08:57:51Z
dc.date.available 2020-05-15T08:57:51Z
dc.date.issued 2013-05
dc.identifier.citation Djidjev, HN.; Onus, Melih, "Scalable and accurate graph clustering and community structure detection" Ieee Transactions On Parallel And Distributed Systems, Vol.24, No.5, pp.1022-1029, (2013) tr_TR
dc.identifier.issn 1045-9219
dc.identifier.uri http://hdl.handle.net/20.500.12416/3843
dc.description.abstract One of the most useful measures of cluster quality is the modularity of the partition, which measures the difference between the number of the edges joining vertices from the same cluster and the expected number of such edges in a random graph. In this paper, we show that the problem of finding a partition maximizing the modularity of a given graph G can be reduced to a minimum weighted cut (MWC) problem on a complete graph with the same vertices as G. We then show that the resulting minimum cut problem can be efficiently solved by adapting existing graph partitioning techniques. Our algorithm finds clusterings of a comparable quality and is much faster than the existing clustering algorithms. tr_TR
dc.language.iso eng tr_TR
dc.publisher IEEE Computer Soc tr_TR
dc.relation.isversionof 10.1109/TPDS.2012.57 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Graph Clustering tr_TR
dc.subject Community Detection tr_TR
dc.subject Graph Partitioning tr_TR
dc.subject Multilevel Algorithms tr_TR
dc.subject Modularity tr_TR
dc.title Scalable and accurate graph clustering and community structure detection tr_TR
dc.type article tr_TR
dc.relation.journal Ieee Transactions On Parallel And Distributed Systems tr_TR
dc.contributor.authorID 103658 tr_TR
dc.identifier.volume 24 tr_TR
dc.identifier.issue 5 tr_TR
dc.identifier.startpage 1022 tr_TR
dc.identifier.endpage 1029 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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