CLIQUE COMMUNITIES IN SOCIAL NETWORKS

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dc.contributor.authorSantos, Jorge
dc.contributor.authorCavique, Luís
dc.contributor.authorMendes, Armando
dc.contributor.editorKun-Huang, Huarng
dc.contributor.editorMoutinho, Luiz
dc.date.accessioned2013-01-23T16:04:15Z
dc.date.available2013-01-23T16:04:15Z
dc.date.issued2012-12
dc.description.abstractThere is a pressing need for new pattern recognition tools and statistical methods to quantify large graphs and predict the behaviour of network systems, due to the large amount of data which can be extracted from the web. In this work a graph mining metric, based on k-clique communities, is used, allowing a better understanding of the network structure. The proposed metric shows that for different graph families correspond different k-clique sequences.por
dc.identifier.authoremailjmas@uevora.pt
dc.identifier.authoremailnd
dc.identifier.authoremailnd
dc.identifier.capituloChapter 20
dc.identifier.citationAU - Armando B. Mendes AU - Jorge M.A. Santos AU - Luís Cavique SP - 469-490 TI - CLIQUE COMMUNITIES IN SOCIAL NETWORKS T2 - Quantitative Modelling in Marketing and Management SN - 978-981-4407-71-7 PB - WORLD SCIENTIFICpor
dc.identifier.doi10.1142/9789814407724_0020
dc.identifier.isbn978-981-4407-71-7
dc.identifier.scientificarea339por
dc.identifier.urihttp://www.worldscientific.com/worldscibooks/10.1142/8503#t=toc
dc.identifier.urihttp://hdl.handle.net/10174/7676
dc.language.isoporpor
dc.publisherWorld Scientific Publishing Co. Pte. Ltd.por
dc.rightsopenAccesspor
dc.subjectData miningpor
dc.subjectsocial networkspor
dc.subjectgraph miningpor
dc.titleCLIQUE COMMUNITIES IN SOCIAL NETWORKSpor
dc.title.alternativeQuantitative Modelling in Marketing and Managementpor
dc.typebookPartpor

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