Detecting the overlapping and hierarchical community structure of complex networks

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dc.creatorLancichinetti, Andrea
dc.creatorFortunato, Santo
dc.creatorKertesz, Janos
dc.date2008-02-08
dc.date2009-03-11
dc.date.accessioned2026-07-07T12:50:42Z
dc.date.available2026-07-07T12:50:42Z
dc.descriptionMany networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling to investigate different hierarchical levels of organization. Tests on real and artificial networks give excellent results.
dc.description20 pages, 8 figures. Final version published on New Journal of Physics
dc.identifierhttps://arxiv.org/abs/0802.1218
dc.identifierhttp://arxiv.org/abs/0802.1218
dc.identifierNew Journal of Physics 11, 033015 (2009)
dc.identifierdoi:10.1088/1367-2630/11/3/033015
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/222761
dc.subjectPhysics and Society
dc.subjectStatistical Mechanics
dc.subjectComputational Physics
dc.subjectComputation
dc.titleDetecting the overlapping and hierarchical community structure of complex networks
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