Functional Multiplex PageRank

TL;DR

This paper introduces the Functional Multiplex PageRank, a new centrality measure for multiplex networks that accounts for multilink patterns and their overlaps, revealing nuanced node importance across diverse complex systems.

Contribution

It proposes a novel centrality measure for multiplex networks that incorporates multilink patterns and their overlaps, enhancing understanding of node importance.

Findings

Reveals differences in central nodes across networks

Shows correlations between node patterns and success

Demonstrates applicability to various real-world networks

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".