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dc.contributor.authorPrat Pérez, Arnau
dc.contributor.authorDomínguez Sal, David
dc.contributor.authorLarriba Pey, Josep
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2015-04-08T12:19:22Z
dc.date.created2014
dc.date.issued2014
dc.identifier.citationPrat, A.; Dominguez, D.; Larriba, J. High quality, scalable and parallel community detection for large real graphs. A: International World Wide Web Conference. "WWW '14: proceedings of the 23rd International Conference on World Wide Web". Seoul: Association for Computing Machinery (ACM), 2014, p. 225-236.
dc.identifier.isbn978-1-4503-2744-2
dc.identifier.urihttp://hdl.handle.net/2117/27168
dc.description.abstractCommunity detection has arisen as one of the most relevant topics in the field of graph mining, principally for its applications in domains such as social or biological networks analysis. Different community detection algorithms have been proposed during the last decade, approaching the problem from different perspectives. However, existing algorithms are, in general, based on complex and expensive computations, making them unsuitable for large graphs with millions of vertices and edges such as those usually found in the real world. In this paper, we propose a novel disjoint community detection algorithm called Scalable Community Detection (SCD). By combining different strategies, SCD partitions the graph by maximizing the Weighted Community Clustering (WCC), a recently proposed community detection metric based on triangle analysis. Using real graphs with ground truth overlapped communities, we show that SCD outperforms the current state of the art proposals (even those aimed at finding overlapping communities) in terms of quality and performance. SCD provides the speed of the fastest algorithms and the quality in terms of NMI and F1Score of the most accurate state of the art proposals. We show that SCD is able to run up to two orders of magnitude faster than practical existing solutions by exploiting the parallelism of current multi-core processors, enabling us to process graphs of unprecedented size in short execution times.
dc.format.extent12 p.
dc.language.isoeng
dc.publisherAssociation for Computing Machinery (ACM)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Sistemes d'informació::Emmagatzematge i recuperació de la informació
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors::Internet
dc.subject.lcshComputer storage devices
dc.subject.lcshWorld Wide Web
dc.subject.otherGraph algorithms
dc.subject.otherCommunity detection
dc.subject.otherClustering
dc.subject.otherParallel
dc.subject.otherSocial networks
dc.subject.otherGraph partition
dc.subject.otherModularity
dc.subject.otherWCC
dc.titleHigh quality, scalable and parallel community detection for large real graphs
dc.typeConference report
dc.subject.lemacOrdinadors -- Dispositius de memòria
dc.subject.lemacWeb
dc.contributor.groupUniversitat Politècnica de Catalunya. DAMA-UPC - Data Management Group
dc.identifier.doi10.1145/2566486.2568010
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://wwwconference.org/proceedings/www2014/starthere.htm
dc.rights.accessRestricted access - publisher's policy
drac.iddocument15347315
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorPrat, A.; Dominguez, D.; Larriba, J.
upcommons.citation.contributorInternational World Wide Web Conference
upcommons.citation.pubplaceSeoul
upcommons.citation.publishedtrue
upcommons.citation.publicationNameWWW '14: proceedings of the 23rd International Conference on World Wide Web
upcommons.citation.startingPage225
upcommons.citation.endingPage236


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Llevat que s'hi indiqui el contrari, els continguts d'aquesta obra estan subjectes a la llicència de Creative Commons: Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya