Functional Multiplex PageRank

¹ School of Mathematical Sciences, Queen Mary University of London - London E1 4NS, UK
² Rome International Centre for Material Science Superstripes RICMASS - 00185 Roma, Italy
³ Departament d'Enginyeria Informática i Matemátiques, Universitat Rovira i Virgili - 43007 Tarragona, Spain

Received: 24 August 2016
Accepted: 15 November 2016

Abstract

Recently it has been recognized that many complex social, technological and biological networks have a multilayer nature and can be described by multiplex networks. Multiplex networks are formed by a set of nodes connected by links having different connotations forming the different layers of the multiplex. Characterizing the centrality of the nodes in a multiplex network is a challenging task since the centrality of the node naturally depends on the importance associated to links of a certain type. Here we propose to assign to each node of a multiplex network a centrality called Functional Multiplex PageRank that is a function of the weights given to every different pattern of connections (multilinks) existent in the multiplex network between any two nodes. Since multilinks distinguish all the possible ways in which the links in different layers can overlap, the Functional Multiplex PageRank can describe important non-linear effects when large relevance or small relevance is assigned to multilinks with overlap. Here we apply the Functional Page Rank to the multiplex airport networks, to the neuronal network of the nematode C. elegans, and to social collaboration and citation networks between scientists. This analysis reveals important differences existing between the most central nodes of these networks, and the correlations between their so-called pattern to success.