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dc.creatorCarrizosa Priego, Emilio Josées
dc.creatorMarín Pérez, Alfredoes
dc.creatorPelegrín García, Mercedeses
dc.date.accessioned2021-04-26T13:16:00Z
dc.date.available2021-04-26T13:16:00Z
dc.date.issued2020-11-03
dc.identifier.citationCarrizosa Priego, E.J., Marín Pérez, A. y Pelegrín García, M. (2020). Spotting Key Members in Networks: Clustering-Embedded Eigenvector Centrality. IEEE Systems Journal, 14 (3), 3916-3925.
dc.identifier.issn1932-8184es
dc.identifier.issn1937-9234es
dc.identifier.urihttps://hdl.handle.net/11441/107837
dc.description.abstractIdentifying key members in a social network is critical to understand the underlying system behavior. Whereas there are several measures designed to discern the most central member, they fail to identify a central set of members and at the same time reveal the spheres of influence of the individuals in such central set. Here, we combine eigenvector centrality with clustering to design a mathematical programming formulation capable of detecting key members while preventing their spheres of influence from overlapping. Our computational experience reproduces these two features as different aspects of the same phenomenon. The optimal set of key members and their spheres of influence are identified in real-life networks and synthetic ones. For the former, community structures are consistent with existing knowledge about the instances. For the latter, network underlying organization is known a priori and it is perfectly uncovered. Experiments further reveal previously neglected nodes to be optimal key members. The size of the instances tested reach several hundreds of nodes and thousands of links.es
dc.formatapplication/pdfes
dc.format.extent9 p.es
dc.language.isoenges
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCes
dc.relation.ispartofIEEE Systems Journal, 14 (3), 3916-3925.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectClusteringes
dc.subjecteigenvector centralityes
dc.subjectmathematical optimizationes
dc.subjectsocial networkses
dc.titleSpotting Key Members in Networks: Clustering-Embedded Eigenvector Centralityes
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Estadística e Investigación Operativaes
dc.relation.publisherversionhttps://doi.org/10.1109/JSYST.2020.2982266es
dc.identifier.doi10.1109/JSYST.2020.2982266es
dc.contributor.groupUniversidad de Sevilla. FQM329: Optimizaciónes
dc.journaltitleIEEE Systems Journales
dc.publication.volumen14es
dc.publication.issue3es
dc.publication.initialPage3916es
dc.publication.endPage3925es

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