Extracting Information from Multiplex Networks

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 ${\tilde{\Theta }}^S$ 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.

11 pages; 5 figures

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• Centralities of Nodes and Influences of Layers in Large Multiplex Networks
• Functional Multiplex PageRank
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• Detecting sequences of system states in temporal networks
• Multilayer Network Analysis for Improved Credit Risk Prediction
• Assessing diversity in multiplex networks
• Layer Communities in Multiplex Networks