Clustering in complex networks. II. Percolation properties

dc.contributor.authorSerrano Moral, Ma. Ángeles (María Ángeles)cat
dc.contributor.authorBoguñá, Mariáncat
dc.date.accessioned2011-07-07T12:53:17Z
dc.date.available2011-07-07T12:53:17Z
dc.date.issued2006
dc.description.abstractThe percolation properties of clustered networks are analyzed in detail. In the case of weak clustering, we present an analytical approach that allows us to find the critical threshold and the size of the giant component. Numerical simulations confirm the accuracy of our results. In more general terms, we show that weak clustering hinders the onset of the giant component whereas strong clustering favors its appearance. This is a direct consequence of the differences in the k-core structure of the networks, which are found to be totally different depending on the level of clustering. An empirical analysis of a real social network confirms our predictions.eng
dc.format.extent8 p.
dc.format.mimetypeapplication/pdf
dc.identifier.idgrec557924
dc.identifier.issn1063-651X
dc.identifier.urihttps://hdl.handle.net/2445/18813
dc.language.isoengeng
dc.publisherThe American Physical Societyeng
dc.relation.isformatofReproducció del document publicat a: http://dx.doi.org/10.1103/PhysRevE.74.056115cat
dc.relation.ispartofPhysical Review E, 2006, vol. 74, núm. 5, p. 056115-1-056115-8
dc.relation.urihttp://dx.doi.org/10.1103/PhysRevE.74.056115
dc.rights(c) American Physical Society, 2006
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.sourceArticles publicats en revistes (Física de la Matèria Condensada)
dc.subject.classificationFísicacat
dc.subject.classificationFísica mèdicacat
dc.subject.classificationSistemes no linealscat
dc.subject.otherPhysicseng
dc.subject.otherMedical physicseng
dc.subject.otherNonlinear systemseng
dc.titleClustering in complex networks. II. Percolation propertieseng
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

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