Extracting Information from Multiplex Networks

TL;DR

This paper explores advanced methods for analyzing multiplex networks, focusing on centrality and community detection, with applications to social science datasets, enhancing understanding of complex interconnected systems.

Contribution

It introduces the Multiplex PageRank algorithm and the indicator function tenilde{ ext{Theta}}^{S}", advancing tools for centrality and mesoscale structure analysis in multiplex networks.

Findings

Multiplex PageRank effectively measures node centrality in multilayer networks.

The indicator function tenilde{ ext{Theta}}^{S} captures mesoscale community structures.

Applications to social science datasets demonstrate the methods' utility.

Abstract

Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from Big Data. For these reasons characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function $\widetilde{\Theta}^{S}$ for describing their mesoscale organization and community structure. As working examples for studying these measures we consider three multiplex network datasets coming for social science.