Exponential Random Simplicial Complexes

TL;DR

This paper extends exponential random graph models to simplicial complexes, providing a maximum-entropy framework for modeling complex systems with independent simplices and analyzing their statistical properties.

Contribution

It introduces the most general random simplicial complex ensemble with independent simplices and characterizes it as a maximum-entropy model under specific constraints.

Findings

Many existing models are special cases of the maximum-entropy framework.

The ensemble with independent simplices maximizes entropy under constraints on simplex counts.

The approach simplifies analysis of complex network models.

Abstract

Exponential random graph models have attracted significant research attention over the past decades. These models are maximum-entropy ensembles under the constraints that the expected values of a set of graph observables are equal to given values. Here we extend these maximum-entropy ensembles to random simplicial complexes, which are more adequate and versatile constructions to model complex systems in many applications. We show that many random simplicial complex models considered in the literature can be casted as maximum-entropy ensembles under certain constraints. We introduce and analyze the most general random simplicial complex ensemble $\mathbf{\Delta}$ with statistically independent simplices. Our analysis is simplified by the observation that any distribution $\mathbb{P}(O)$ on any collection of objects $\mathcal{O}=\{O\}$, including graphs and simplicial complexes, is maximum-entropy under the constraint that the expected value of $-\ln \mathbb{P}(O)$ is equal to the entropy of the distribution. With the help of this observation, we prove that ensemble $\mathbf{\Delta}$ is maximum-entropy under two types of constraints that fix the expected numbers of simplices and their boundaries.