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dc.contributor.authorPrat Pérez, Arnau
dc.contributor.authorDomínguez Sal, David
dc.contributor.authorBrunat Blay, Josep Maria
dc.contributor.authorLarriba Pey, Josep
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2016-09-08T08:14:33Z
dc.date.available2016-09-08T08:14:33Z
dc.date.issued2016-02-01
dc.identifier.citationPrat, A., Domínguez, D., Brunat, Josep M., Larriba, J. Put three and three together: Triangle-driven community detection. "ACM transactions on knowledge discovery from data", 1 Febrer 2016, vol. 10, núm. 3, p. 22:1-22:42.
dc.identifier.issn1556-4681
dc.identifier.urihttp://hdl.handle.net/2117/89696
dc.description.abstractCommunity detection has arisen as one of the most relevant topics in the field of graph data mining due to its applications in many fields such as biology, social networks, or network traffic analysis. Although the existing metrics used to quantify the quality of a community work well in general, under some circumstances, they fail at correctly capturing such notion. The main reason is that these metrics consider the internal community edges as a set, but ignore how these actually connect the vertices of the community. We propose the Weighted Community Clustering (WCC), which is a new community metric that takes the triangle instead of the edge as the minimal structural motif indicating the presence of a strong relation in a graph. We theoretically analyse WCC in depth and formally prove, by means of a set of properties, that the maximization of WCC guarantees communities with cohesion and structure. In addition, we propose Scalable Community Detection (SCD), a community detection algorithm based on WCC, which is designed to be fast and scalable on SMP machines, showing experimentally that WCC correctly captures the concept of community in social networks using real datasets. Finally, using ground-truth data, we show that SCD provides better quality than the best disjoint community detection algorithms of the state of the art while performing faster.
dc.language.isoeng
dc.publisherAssociation for Computing Machinery (ACM)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica
dc.subject.lcshWeb usage mining
dc.subject.lcshSocial networks
dc.subject.lcshGraph algorithms
dc.subject.otherCommunity detection
dc.subject.otherParallel algorithm
dc.subject.otherScalable algorithm
dc.subject.otherTriangles
dc.subject.otherComplex networks
dc.titlePut three and three together: Triangle-driven community detection
dc.typeArticle
dc.subject.lemacMineria de web
dc.subject.lemacXarxes socials
dc.contributor.groupUniversitat Politècnica de Catalunya. MD - Matemàtica Discreta
dc.contributor.groupUniversitat Politècnica de Catalunya. DAMA-UPC - Data Management Group
dc.identifier.doi10.1145/2775108
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
local.identifier.drac18545347
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MICINN/6PN/MTM2011-24097
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TIN2013-47008-R
local.citation.authorPrat, A.; Domínguez, D.; Brunat, Josep M.; Larriba, J.
local.citation.publicationNameACM transactions on knowledge discovery from data
local.citation.volume10
local.citation.number3
local.citation.startingPage22:1
local.citation.endingPage22:42


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