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

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

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dc.contributor.author Agryzkov, Taras es_ES
dc.contributor.author Curado, Manuel es_ES
dc.contributor.author Pedroche Sánchez, Francisco es_ES
dc.contributor.author Tortosa, Leandro es_ES
dc.contributor.author Vicent, Jose F. es_ES
dc.date.accessioned 2020-04-07T05:49:31Z
dc.date.available 2020-04-07T05:49:31Z
dc.date.issued 2019-02 es_ES
dc.identifier.uri http://hdl.handle.net/10251/140416
dc.description.abstract [EN] Usually, 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.sponsorship This research is partially supported by the Spanish Government, Ministerio de Economia y Competividad, grant number TIN2017-84821-P. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Symmetry (Basel) es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Adapted PageRank algorithm es_ES
dc.subject PageRank vector es_ES
dc.subject Networks centrality es_ES
dc.subject Multiplex networks es_ES
dc.subject Biplex networks es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Extending the Adapted PageRank Algorithm Centrality to Multiplex Networks with Data Using the PageRank Two-Layer Approach es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/sym11020284 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84821-P/ES/ANALISIS Y VISUALIZACION DE LA CIUDAD COMO UNA RED MULTIPLE DE DATOS Y SU IMPLICACION EN EL TURISMO./ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.description.bibliographicCitation Agryzkov, T.; Curado, M.; Pedroche Sánchez, F.; Tortosa, L.; Vicent, JF. (2019). Extending the Adapted PageRank Algorithm Centrality to Multiplex Networks with Data Using the PageRank Two-Layer Approach. Symmetry (Basel). 11(2):1-17. https://doi.org/10.3390/sym11020284 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/sym11020284 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 17 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11 es_ES
dc.description.issue 2 es_ES
dc.identifier.eissn 2073-8994 es_ES
dc.relation.pasarela S\388353 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
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