Graph matching based on similarities in structure and attributes

TL;DR

The paper introduces GASM, a graph matching algorithm that integrates structural and attribute information to improve matching quality, outperforming existing methods even without attributes.

Contribution

The novel GASM algorithm unifies structural and attribute data for graph matching, with adjustable parameters for reliability, achieving superior or comparable results to state-of-the-art methods.

Findings

GASM outperforms existing algorithms in matching quality.

GASM achieves similar processing times to state-of-the-art methods.

Effective even without attribute information.

Abstract

Finding vertex-to-vertex correspondences in real-world graphs is a challenging task with applications in a wide variety of domains. Structural matching based on graphs connectivities has attracted considerable attention, while the integration of all the other information stemming from vertices and edges attributes has been mostly left aside. Here we present the Graph Attributes and Structure Matching (GASM) algorithm, which provides high-quality solutions by integrating all the available information in a unified framework. Parameters quantifying the reliability of the attributes can tune how much the solutions should rely on the structure or on the attributes. We further show that even without attributes GASM consistently finds as-good-as or better solutions than state-of-the-art algorithms, with similar processing times.