Counting Homomorphisms and Partition Functions

TL;DR

This paper explores the complexity of counting homomorphisms and their weighted variants (partition functions) between graphs, providing a survey of known results and a simplified proof of a classification theorem for non-negative matrices.

Contribution

It offers a more accessible, graph-theoretic proof of Bulatov and Grohe's classification theorem for the complexity of partition functions with non-negative matrices.

Findings

Classification of the complexity of partition functions with non-negative matrices

Simplified proof using graph-theoretic language

Survey of existing results on homomorphism counting

Abstract

Homomorphisms between relational structures are not only fundamental mathematical objects, but are also of great importance in an applied computational context. Indeed, constraint satisfaction problems (CSPs), a wide class of algorithmic problems that occur in many different areas of computer science such as artificial intelligence or database theory, may be viewed as asking for homomorphisms between two relational structures [FedVar98]. In a logical setting, homomorphisms may be viewed as witnesses for positive primitive formulas in a relational language. As we shall see, homomorphisms, or more precisely the numbers of homomorphisms between two structures, are also related to a fundamental computational problem of statistical physics. In this article, we are concerned with the complexity of counting homomorphisms from a given structure A to a fixed structure B. Actually, we are mainly interested in a generalization of this problem to weighted homomorphisms (or partition functions). We almost exclusively focus on graphs. The first part of the article is a short survey of what is known about the problem. In the second part, we give a proof of a theorem due to Bulatov and the first author of this paper [BulGro05], which classifies the complexity of partition functions described by matrices with non-negative entries. The proof we give here is essentially the same as the original one, with a few shortcuts due to [Thu09], but it is phrased in a different, more graph theoretical language that may make it more accessible to most readers.