Next Article in Journal
A Generic Framework for Accountable Optimistic Fair Exchange Protocol
Next Article in Special Issue
Derivative Free Fourth Order Solvers of Equations with Applications in Applied Disciplines
Previous Article in Journal
An Upgraded Version of the Binary Search Space-Structured VQ Search Algorithm for AMR-WB Codec
Previous Article in Special Issue
A Transmission Prediction Neighbor Mechanism Based on a Mixed Probability Model in an Opportunistic Complex Social Network
 
 
Article

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

1
Campus de San Vicente, Department of Computer Science and Artificial Intelligence, University of Alicante, Ap. Correos 99, E-03080 Alicante, Spain
2
Campus de los Canteros, Departament of Technology, Catholic University of Ávila, Los Canteros s/n, E-05005 Ávila, Spain
3
Institut de Matemàtica Multidisciplinària, Universitat Politècnica de València, Camí de Vera s/n, E-46022 València, Spain
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(2), 284; https://doi.org/10.3390/sym11020284
Received: 25 January 2019 / Revised: 18 February 2019 / Accepted: 19 February 2019 / Published: 22 February 2019
(This article belongs to the Special Issue Symmetry in Complex Systems)
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. View Full-Text
Keywords: adapted PageRank algorithm; PageRank vector; networks centrality; multiplex networks; biplex networks adapted PageRank algorithm; PageRank vector; networks centrality; multiplex networks; biplex networks
Show Figures

Figure 1

MDPI and ACS Style

Agryzkov, T.; Curado, M.; Pedroche, F.; Tortosa, L.; Vicent, J.F. Extending the Adapted PageRank Algorithm Centrality to Multiplex Networks with Data Using the PageRank Two-Layer Approach. Symmetry 2019, 11, 284. https://doi.org/10.3390/sym11020284

AMA Style

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. https://doi.org/10.3390/sym11020284

Chicago/Turabian Style

Agryzkov, Taras, Manuel Curado, Francisco Pedroche, Leandro Tortosa, and José F. Vicent. 2019. "Extending the Adapted PageRank Algorithm Centrality to Multiplex Networks with Data Using the PageRank Two-Layer Approach" Symmetry 11, no. 2: 284. https://doi.org/10.3390/sym11020284

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop