Graph Isomorphism: Mixed-Integer Convex Optimization from First-Order Methods

TL;DR

This paper introduces a novel convex mixed-integer optimization approach for the graph isomorphism problem, leveraging first-order methods and variable fixing techniques to improve detection performance.

Contribution

It presents a new convex mixed-integer formulation for GI and demonstrates its effectiveness over existing methods through extensive computational evaluation.

Findings

Optimization-based methods benefit from high symmetry in graphs.

Variable fixing techniques are crucial for asymmetric instances.

The proposed method outperforms existing approaches on several graph families.

Abstract

The graph isomorphism (GI) problem, which asks whether two graphs are structurally identical, occupies a unique position in computational complexity -- it is neither known to be solvable in polynomial time, nor proven to be NP-complete. We propose a convex mixed-integer formulation of the problem and leverage first-order convex optimization to tackle it, following a stream of recent work on optimization-driven graph isomorphism detection. We strengthen our formulation with variable fixing techniques that prove highly effective while preserving the polyhedral structure. We perform extensive computations evaluating the performance of different families of methods including a mixed-integer convex formulation, mixed-integer linear optimization, local search and spectral heuristics over a collection of challenging GI instances. We find that a high level of symmetry is beneficial for optimization-based methods. On the other hand, presolving techniques that detect local substructures to fix variables are crucial for asymmetric instances. The proposed method outperforms the second best approach, the integer feasibility approach, on 6 of the 12 graphs families and is on par with it on symmetric families.