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dc.contributor.authorGuerrero López, Manuel Alejandro
dc.contributor.authorGil Montoya, Consolación
dc.contributor.authorGil Montoya, Francisco
dc.contributor.authorAlcayde García, Alfredo
dc.contributor.authorBaños Navarro, Raúl
dc.date.accessioned2020-11-23T12:08:08Z
dc.date.available2020-11-23T12:08:08Z
dc.date.issued2020-11-17
dc.identifier.issn2227-7390
dc.identifier.urihttp://hdl.handle.net/10835/8922
dc.description.abstractReal-world complex systems are often modeled by networks such that the elements are represented by vertices and their interactions are represented by edges. An important characteristic of these networks is that they contain clusters of vertices densely linked amongst themselves and more sparsely connected to nodes outside the cluster. Community detection in networks has become an emerging area of investigation in recent years, but most papers aim to solve single-objective formulations, often focused on optimizing structural metrics, including the modularity measure. However, several studies have highlighted that considering modularityas a unique objective often involves resolution limit and imbalance inconveniences. This paper opens a new avenue of research in the study of multi-objective variants of the classical community detection problem by applying multi-objective evolutionary algorithms that simultaneously optimize different objectives. In particular, they analyzed two multi-objective variants involving not only modularity but also the conductance metric and the imbalance in the number of nodes of the communities. With this aim, a new Pareto-based multi-objective evolutionary algorithm is presented that includes advanced initialization strategies and search operators. The results obtained when solving large-scale networks representing real-life power systems show the good performance of these methods and demonstrate that it is possible to obtain a balanced number of nodes in the clusters formed while also having high modularity and conductance values.es_ES
dc.language.isoenes_ES
dc.publisherMDPIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectnetwork optimizationes_ES
dc.subjectcommunity detectiones_ES
dc.subjectmodularityes_ES
dc.subjectimbalancees_ES
dc.subjectconductancees_ES
dc.subjectmulti-objective evolutionary algorithmses_ES
dc.titleMulti-Objective Evolutionary Algorithms to Find Community Structures in Large Networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://www.mdpi.com/2227-7390/8/11/2048es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional