Second-order Markov random fields for independent sets on the infinite Cayley tree

TL;DR

This paper investigates second-order Markov random fields on independent sets of the infinite Cayley tree, establishing conditions for phase transitions, uniqueness of Gibbs measures, and the effects of higher-order interactions.

Contribution

It extends understanding of M.r.f. with second-order interactions on infinite trees, providing conditions for uniqueness, phase transitions, and robustness of Gibbs measures.

Findings

Proves FKG inequality under log-convexity of potentials.

Provides necessary and sufficient conditions for Gibbs measure uniqueness.

Characterizes phase transition and bounds on critical activity.

Abstract

Recently, there has been significant interest in understanding the properties of Markov random fields (M.r.f.) defined on the independent sets of sparse graphs. When these M.r.f. are restricted to pairwise interactions (i.e. hardcore model), much progress has been made. However, considerably less is known in the presence of higher-order interactions, which arise e.g. in the analysis of independent sets with special properties and the study of resource-constrained communication networks. In this paper, we further our understanding of such models by analyzing M.r.f. with second-order interactions on the independent sets of the infinite Cayley tree. We prove that the associated Gibbsian specification satisfies the celebrated FKG Inequality whenever the local potentials defining the Hamiltonian satisfy a log-convexity condition. Under this condition, we give necessary and sufficient conditions for the existence of a unique infinite-volume Gibbs measure in terms of an explicit system of equations, prove the existence of a phase transition, and give explicit bounds on the associated critical activity, which we prove to exhibit a certain robustness. For potentials which are small perturbations of those coinciding to the hardcore model at the critical activity, we characterize whether the resulting specification has a unique infinite-volume Gibbs measure in terms of whether these perturbations satisfy an explicit linear inequality. Our analysis reveals an interesting non-monotonicity with regards to biasing towards excluded nodes with no included neighbors.