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dc.contributor.authorAldecoa, Rodrigo-
dc.contributor.authorMarín, Ignacio-
dc.date.accessioned2013-09-09T08:13:20Z-
dc.date.available2013-09-09T08:13:20Z-
dc.date.issued2013-07-17-
dc.identifier.citationScientific Reports 3: 2216 (2013)es_ES
dc.identifier.urihttp://hdl.handle.net/10261/81631-
dc.description11 páginas, 8 figuras, 1 tabla.es_ES
dc.description.abstractThe characterization of network community structure has profound implications in several scientific areas. Therefore, testing the algorithms developed to establish the optimal division of a network into communities is a fundamental problem in the field. We performed here a highly detailed evaluation of community detection algorithms, which has two main novelties: 1) using complex closed benchmarks, which provide precise ways to assess whether the solutions generated by the algorithms are optimal; and, 2) A novel type of analysis, based on hierarchically clustering the solutions suggested by multiple community detection algorithms, which allows to easily visualize how different are those solutions. Surprise, a global parameter that evaluates the quality of a partition, confirms the power of these analyses. We show that none of the community detection algorithms tested provide consistently optimal results in all networks and that Surprise maximization, obtained by combining multiple algorithms, obtains quasi-optimal performances in these difficult benchmarks.es_ES
dc.description.sponsorshipThis study was supported by grant BFU2011-30063 (Spanish government).es_ES
dc.language.isoenges_ES
dc.publisherNature Publishing Groupes_ES
dc.rightsopenAccesses_ES
dc.titleExploring the limits of community detection strategies in complex networkses_ES
dc.typeartículoes_ES
dc.identifier.doi10.1038/srep02216-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttp://dx.doi.org/10.1038/srep02216es_ES
dc.identifier.e-issn2045-2322-
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