Statistical Mechanics of Multiplex Ensembles: Entropy and Overlap

TL;DR

This paper develops a statistical mechanics framework for multiplex networks, capturing link overlaps and correlations across layers, and provides entropy formulas for different ensemble types to aid inference tasks.

Contribution

It introduces correlated multiplex ensembles with link overlap considerations and derives entropy expressions for microcanonical and canonical cases.

Findings

Defined correlated multiplex ensembles with link overlap.

Derived entropy formulas for different multiplex ensembles.

Provided tools for inference in multiplex network analysis.

Abstract

There is growing interest in multiplex networks where individual nodes take part in several layers of networks simultaneously. This is the case for example in social networks where each individual node has different kind of social ties or transportation systems where each location is connected to another location by different types of transport. Many of these multiplex are characterized by a significant overlap of the links in different layers. In this paper we introduce a statistical mechanics framework to describe multiplex ensembles. A multiplex is a system formed by N nodes and M layers of interactions where each node belongs to the M layers at the same time. Each layer $\alpha$ is formed by a network $G^{\alpha}$. Here we introduce the concept of correlated multiplex ensembles in which the existence of a link in one layer is correlated with the existence of a link in another layer. This implies that a typical multiplex of the ensemble can have a significant overlap of the links in the different layers. Moreover we characterize microcanonical and canonical multiplex ensembles satisfying respectively hard and soft constraints and we discuss how to construct multiplex in these ensembles. Finally we provide the expression for the entropy of these ensembles that can be useful to address different inference problems involving multiplexes.