Graph Homomorphism Polynomials: Algorithms and Complexity

TL;DR

This paper characterizes the complexity of homomorphism polynomials based on graph parameters and provides a unified framework that connects various known results and introduces new algorithms for graph pattern detection.

Contribution

It establishes a novel characterization of polynomial complexity classes via graph parameters and unifies multiple results in graph pattern enumeration and complexity theory.

Findings

Complexity classes correspond to graph parameters: treedepth, pathwidth, and treewidth.

Superpolynomial separations between circuit, ABP, and formula complexities in the monotone setting.

New space-time efficient algorithms for pattern detection and counting.

Abstract

We study homomorphism polynomials, which are polynomials that enumerate all homomorphisms from a pattern graph $H$ to $n$-vertex graphs. These polynomials have received a lot of attention recently for their crucial role in several new algorithms for counting and detecting graph patterns, and also for obtaining natural polynomial families which are complete for algebraic complexity classes $\mathsf{VBP}$, $\mathsf{VP}$, and $\mathsf{VNP}$. We discover that, in the monotone setting, the formula complexity, the ABP complexity, and the circuit complexity of such polynomial families are exactly characterized by the treedepth, the pathwidth, and the treewidth of the pattern graph respectively. Furthermore, we establish a single, unified framework, using our characterization, to collect several known results that were obtained independently via different methods. For instance, we attain superpolynomial separations between circuits, ABPs, and formulas in the monotone setting, where the polynomial families separating the classes all correspond to well-studied combinatorial problems. Moreover, our proofs rediscover fine-grained separations between these models for constant-degree polynomials. The characterization additionally yields new space-time efficient algorithms for several pattern detection and counting problems.