Graph matching beyond perfectly-overlapping Erd\H{o}s--R\'enyi random graphs

TL;DR

This paper introduces new graph matching methods that leverage degree information for partially-overlapping graphs and stochastic block models, demonstrating superior performance in real-world datasets.

Contribution

Proposes the edge exploited degree profile graph matching method and two refined variations for more effective matching in complex scenarios.

Findings

Methods outperform state-of-the-art techniques.

Effective in real-world datasets like zebrafish neuron activity.

Applicable to partially-overlapping and stochastic block model graphs.

Abstract

Graph matching is a fruitful area in terms of both algorithms and theories. In this paper, we exploit the degree information, which was previously used only in noiseless graphs and perfectly-overlapping Erd\H{o}s--R\'enyi random graphs matching. We are concerned with graph matching of partially-overlapping graphs and stochastic block models, which are more useful in tackling real-life problems. We propose the edge exploited degree profile graph matching method and two refined varations. We conduct a thorough analysis of our proposed methods' performances in a range of challenging scenarios, including a zebrafish neuron activity data set and a coauthorship data set. Our methods are proved to be numerically superior than the state-of-the-art methods.