IRMA: Iterative Repair for graph MAtching

TL;DR

IRMA is an iterative graph matching algorithm that enhances the accuracy of seed-based alignment in scale-free networks by repairing mistakes over multiple iterations, outperforming existing methods with minimal additional computational cost.

Contribution

IRMA introduces an iterative repair process that improves upon percolation-based graph alignment algorithms, especially for scale-free networks, with theoretical guarantees and practical performance gains.

Findings

IRMA outperforms state-of-the-art algorithms in accuracy.

Iterative repair increases long-term recall and precision.

Parallel implementation reduces runtime.

Abstract

The alignment of two similar graphs from different domains is a well-studied problem. In many practical usages, there is no reliable information or labels over the vertices or edges, leaving structural similarity as the only information available to match such a graph. In such cases, one often assumes a small amount of already aligned vertices -- called a seed. Current state-of-the-art scalable seeded alignment algorithms are based on percolation. Namely, aligned vertices are used to align their neighbors and gradually percolate in parallel in both graphs. However, percolation-based graph alignment algorithms are still limited in scale-free degree distributions. We here propose `IRMA' -- Iterative Repair for graph MAtching to show that the accuracy of percolation-based algorithms can be improved in real-world graphs with a limited additional computational cost, and with lower run time when used in a parallel version. IRMA starts by creating a primary alignment using an existing percolation algorithm, then it iteratively repairs the mistakes in the previous alignment steps. We prove that IRMA improves on single-iteration algorithms. We then numerically show that it is significantly better than all state-of-the-art seeded graph alignment algorithms on the graphs that they tested. In scale-free networks, many vertices have a very low degree. Such vertices have a high probability of erroneous alignments. We show that combining iterations with high recall but low precision in the alignment leads in the long run to higher recall and precision for the entire alignment.