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

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Título: Extending the Adapted PageRank Algorithm Centrality to Multiplex Networks with Data Using the PageRank Two-Layer Approach
Autor/es: Agryzkov, Taras | Curado, Manuel | Pedroche, Francisco | Tortosa, Leandro | Vicent, Jose F.
Grupo/s de investigación o GITE: Análisis y Visualización de Datos en Redes (ANVIDA)
Centro, Departamento o Servicio: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Palabras clave: Adapted PageRank algorithm | PageRank vector | Networks centrality | Multiplex networks | Biplex networks
Área/s de conocimiento: Ciencia de la Computación e Inteligencia Artificial
Fecha de publicación: 22-feb-2019
Editor: MDPI
Cita bibliográfica: Agryzkov 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/sym11020284
Resumen: 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.
Patrocinador/es: This research is partially supported by the Spanish Government, Ministerio de Economía y Competividad, grant number TIN2017-84821-P.
URI: http://hdl.handle.net/10045/88734
ISSN: 2073-8994
DOI: 10.3390/sym11020284
Idioma: eng
Tipo: info:eu-repo/semantics/article
Derechos: © 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/).
Revisión científica: si
Versión del editor: https://doi.org/10.3390/sym11020284
Aparece en las colecciones:INV - ANVIDA - Artículos de Revistas

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