Forest expansion of two-body partition functions for sparse interaction graphs

TL;DR

This paper introduces a forest expansion method for approximating two-body partition functions on sparse graphs, enabling efficient high-temperature calculations using belief propagation and spanning tree algorithms.

Contribution

It develops a novel forest expansion approach for two-body partition functions on sparse graphs, including explicit correction terms and approximation schemes.

Findings

Partition function approximated by forest expansion above temperature T*

High-temperature models solvable via belief propagation on sparse graphs

Existence of regimes where maximal spanning trees provide the reference structure

Abstract

We study tree approximations to classical two-body partition functions on sparse and loopy graphs via the Brydges-Kennedy-Abdessalam-Rivasseau forest expansion. We show that for sparse graphs (with large cycles), the partition function above a certain temperature $T^*$ can be approximated by a graph polynomial expansion over forests of the interaction graph. Within this "forest phase", we show that the approximation can be written in terms of a reference tree $\mathcal T$ on the interaction graph, with corrections due to cycles. From this point of view, this implies that high-temperature models are easy to solve on sparse graphs, as one can evaluate the partition function using belief propagation. We also show that there exists a high- and low-temperature regime, in which $\mathcal T$ can be obtained via a maximal spanning tree algorithm on a (given) weighted graph. We study the algebra of these corrections and provide first- and second-order approximation to the tree Ansatz, and give explicit examples for the first-order approximation.