An Eigenvector Centrality for Multiplex Networks with Data

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Title: An Eigenvector Centrality for Multiplex Networks with Data
Authors: Pedroche, Francisco | Tortosa, Leandro | Vicent, Jose F.
Research Group/s: Análisis y Visualización de Datos en Redes (ANVIDA)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Eigenvector centrality | Networks centrality | Two-layer approach PageRank | Multiplex networks | Biplex networks
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 5-Jun-2019
Publisher: MDPI
Citation: Pedroche F, Tortosa L, Vicent JF. An Eigenvector Centrality for Multiplex Networks with Data. Symmetry. 2019; 11(6):763. doi:10.3390/sym11060763
Abstract: Networks are useful to describe the structure of many complex systems. Often, understanding these systems implies the analysis of multiple interconnected networks simultaneously, since the system may be modelled by more than one type of interaction. Multiplex networks are structures capable of describing networks in which the same nodes have different links. Characterizing the centrality of nodes in multiplex networks is a fundamental task in network theory. In this paper, we design and discuss a centrality measure for multiplex networks with data, extending the concept of eigenvector centrality. The essential feature that distinguishes this measure is that it calculates the centrality in multiplex networks where the layers show different relationships between nodes and where each layer has a dataset associated with the nodes. The proposed model is based on an eigenvector centrality for networks with data, which is adapted according to the idea behind the two-layer approach PageRank. The core of the centrality proposed is the construction of an irreducible, non-negative and primitive matrix, whose dominant eigenpair provides a node classification. Several examples show the characteristics and possibilities of the new centrality illustrating some applications.
Sponsor: This research is partially supported by the Spanish Government, Ministerio de Economía y Competividad, grant number TIN2017-84821-P.
ISSN: 2073-8994
DOI: 10.3390/sym11060763
Language: eng
Type: info:eu-repo/semantics/article
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 (
Peer Review: si
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Appears in Collections:INV - ANVIDA - Artículos de Revistas

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