Extending the Adapted PageRank Algorithm Centrality to Multiplex Networks with Data Using the PageRank Two-Layer Approach

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dc.contributorAnálisis y Visualización de Datos en Redes (ANVIDA)es_ES
dc.contributor.authorAgryzkov, Taras-
dc.contributor.authorCurado, Manuel-
dc.contributor.authorPedroche, Francisco-
dc.contributor.authorTortosa, Leandro-
dc.contributor.authorVicent, Jose F.-
dc.contributor.otherUniversidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificiales_ES
dc.identifier.citationAgryzkov T, Curado M, Pedroche F, Tortosa L, Vicent JF. Extending the Adapted PageRank Algorithm Centrality to Multiplex Networks with Data Using the PageRank Two-Layer Approach. Symmetry. 2019; 11(2):284. doi:10.3390/sym11020284es_ES
dc.description.abstractUsually, the nodes’ interactions in many complex networks need a more accurate mapping than simple links. For instance, in social networks, it may be possible to consider different relationships between people. This implies the use of different layers where the nodes are preserved and the relationships are diverse, that is, multiplex networks or biplex networks, for two layers. One major issue in complex networks is the centrality, which aims to classify the most relevant elements in a given system. One of these classic measures of centrality is based on the PageRank classification vector used initially in the Google search engine to order web pages. The PageRank model may be understood as a two-layer network where one layer represents the topology of the network and the other layer is related to teleportation between the nodes. This approach may be extended to define a centrality index for multiplex networks based on the PageRank vector concept. On the other hand, the adapted PageRank algorithm (APA) centrality constitutes a model to obtain the importance of the nodes in a spatial network with the presence of data (both real and virtual). Following the idea of the two-layer approach for PageRank centrality, we can consider the APA centrality under the perspective of a two-layer network where, on the one hand, we keep maintaining the layer of the topological connections of the nodes and, on the other hand, we consider a data layer associated with the network. Following a similar reasoning, we are able to extend the APA model to spatial networks with different layers. The aim of this paper is to propose a centrality measure for biplex networks that extends the adapted PageRank algorithm centrality for spatial networks with data to the PageRank two-layer approach. Finally, we show an example where the ability to analyze data referring to a group of people from different aspects and using different sets of independent data are revealed.es_ES
dc.description.sponsorshipThis research is partially supported by the Spanish Government, Ministerio de Economía y Competividad, grant number TIN2017-84821-P.es_ES
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).es_ES
dc.subjectAdapted PageRank algorithmes_ES
dc.subjectPageRank vectores_ES
dc.subjectNetworks centralityes_ES
dc.subjectMultiplex networkses_ES
dc.subjectBiplex networkses_ES
dc.subject.otherCiencia de la Computación e Inteligencia Artificiales_ES
dc.titleExtending the Adapted PageRank Algorithm Centrality to Multiplex Networks with Data Using the PageRank Two-Layer Approaches_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84821-P-
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