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Article

An Eigenvector Centrality for Multiplex Networks with Data

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Institut de Matemàtica Multidisciplinària, Universitat Politècnica de València, E-46022 València, Spain
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Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente, Ap. Correos 99, E-03080 Alicante, Spain
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(6), 763; https://doi.org/10.3390/sym11060763
Received: 10 May 2019 / Revised: 29 May 2019 / Accepted: 3 June 2019 / Published: 5 June 2019
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. View Full-Text
Keywords: eigenvector centrality; networks centrality; two-layer approach PageRank; multiplex networks; biplex networks eigenvector centrality; networks centrality; two-layer approach PageRank; multiplex networks; biplex networks
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MDPI and ACS Style

Pedroche, F.; Tortosa, L.; Vicent, J.F. An Eigenvector Centrality for Multiplex Networks with Data. Symmetry 2019, 11, 763. https://doi.org/10.3390/sym11060763

AMA Style

Pedroche F, Tortosa L, Vicent JF. An Eigenvector Centrality for Multiplex Networks with Data. Symmetry. 2019; 11(6):763. https://doi.org/10.3390/sym11060763

Chicago/Turabian Style

Pedroche, Francisco, Leandro Tortosa, and José F. Vicent. 2019. "An Eigenvector Centrality for Multiplex Networks with Data" Symmetry 11, no. 6: 763. https://doi.org/10.3390/sym11060763

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