Statistical physics of exchangeable sparse simple networks, multiplex networks and simplicial complexes

TL;DR

This paper develops a statistical physics framework and algorithms for creating exchangeable sparse network ensembles, including complex structures like multiplex networks and simplicial complexes, with heterogeneous degree distributions.

Contribution

It introduces a general theoretical framework for exchangeable sparse networks, extending it to complex structures like multiplex networks and simplicial complexes using statistical mechanics.

Findings

Framework generates networks with heterogeneous degree distributions.

Extends to correlated and directed networks, bipartite, multiplex, and simplicial complexes.

Provides a Metropolis-Hastings algorithm for sampling exchangeable networks.

Abstract

Exchangeability is a desired statistical property of network ensembles requiring their invariance upon relabelling of the nodes. However combining sparsity of network ensembles with exchangeability is challenging. Here we propose a statistical physics framework and a Metropolis-Hastings algorithm defining exchangeable sparse network ensembles. The model generates networks with heterogeneous degree distributions by enforcing only global constraints while existing (non exchangeable) exponential random graphs enforce an extensive number of local constraints. This very general theoretical framework to describe exchangeable networks is here first formulated for uncorrelated simple networks and then it is extended to treat simple networks with degree correlations, directed networks, bipartite networks and generalized network structures including multiplex networks and simplicial complexes. In particular here we formulate and treat both uncorrelated and correlated exchangeable ensembles of simplicial complexes using statistical mechanics approaches.