Combinatorial Analysis of Multiple Networks

TL;DR

This paper introduces a layered network model combining online and offline relationships, along with new analysis tools and a betweenness centrality measure for multi-layer networks, highlighting the need for further data and methods.

Contribution

It presents a real-world multi-layer network model, innovative analysis tools, and a new betweenness centrality concept tailored for multiple networks.

Findings

Identification of hidden motifs across layers

Proposal of a new betweenness centrality measure

Preliminary evidence of layered network correlations

Abstract

The study of complex networks has been historically based on simple graph data models representing relationships between individuals. However, often reality cannot be accurately captured by a flat graph model. This has led to the development of multi-layer networks. These models have the potential of becoming the reference tools in network data analysis, but require the parallel development of specific analysis methods explicitly exploiting the information hidden in-between the layers and the availability of a critical mass of reference data to experiment with the tools and investigate the real-world organization of these complex systems. In this work we introduce a real-world layered network combining different kinds of online and offline relationships, and present an innovative methodology and related analysis tools suggesting the existence of hidden motifs traversing and correlating different representation layers. We also introduce a notion of betweenness centrality for multiple networks. While some preliminary experimental evidence is reported, our hypotheses are still largely unverified, and in our opinion this calls for the availability of new analysis methods but also new reference multi-layer social network data.