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Clique communities in social networks

dc.contributor.authorCavique, Luís
dc.contributor.authorMendes, Armando B.
dc.contributor.authorSantos, Jorge M. A.
dc.date.accessioned2011-09-07T13:16:51Z
dc.date.available2011-09-07T13:16:51Z
dc.date.issued2011
dc.description.abstractGiven the large amount of data provided by the Web 2.0, there is a pressing need to obtain new metrics to better understand the network structure; how their communities are organized and the way they evolve over time. Complex network and graph mining metrics are essentially based on low complexity computational procedures like the diameter of the graph, clustering coefficient and the degree distribution of the nodes. The connected communities in the social networks have, essentially, been studied in two contexts: global metrics like the clustering coefficient and the node groups, such as the graph partitions and clique communities.por
dc.identifier.urihttp://hdl.handle.net/10400.2/1852
dc.language.isoengpor
dc.peerreviewedyespor
dc.subjectSocial networkspor
dc.subjectClique communitiespor
dc.titleClique communities in social networkspor
dc.typeworking paper
dspace.entity.typePublication
oaire.citation.endPage22por
oaire.citation.startPage1por
person.familyNameCavique
person.familyNameB Mendes
person.familyNameSantos
person.givenNameLuís
person.givenNameArmando
person.givenNameJorge
person.identifier1008054
person.identifier.ciencia-id911E-84AC-3956
person.identifier.ciencia-idEE1E-90E7-2751
person.identifier.ciencia-id8913-96E9-2FEE
person.identifier.orcid0000-0002-5590-1493
person.identifier.orcid0000-0003-3049-5852
person.identifier.orcid0000-0003-1000-8252
person.identifier.ridN-7280-2015
person.identifier.ridN-7809-2013
person.identifier.scopus-author-id13003839500
person.identifier.scopus-author-id55462369200
rcaap.rightsopenAccesspor
rcaap.typeworkingPaperpor
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relation.isAuthorOfPublication.latestForDiscovery40906a16-46a2-42f1-b26d-7db7012294ee

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