GRAIL: Graph Edit Distance and Node Alignment using LLM-Generated Code

Samidha Verma, Arushi Goyal, Ananya Mathur, Ankit Anand, Sayan Ranu

Track: long paper (up to 9 pages)

Keywords: Graph Edit Distance, Large Language Model, Code Discovery, Cross-domain generalization, Interpretability

TL;DR: GRAIL leverages LLMs and automated prompt engineering, guided by an evolutionary algorithm, to generate programs for accurate, cross-domain generalizable, and interpretable Graph Edit Distance computation.

Abstract: Graph Edit Distance (GED) is a widely used metric for measuring similarity between two graphs. Computing the optimal GED is NP-hard, leading to the development of various neural and non-neural heuristics. While neural methods have achieved improved approximation quality compared to non-neural approaches, they face significant challenges: (1) They require large amounts of ground truth data, which is itself NP-hard to compute. (2) They operate as black boxes, offering limited interpretability. (3) They lack cross-domain generalization, necessitating expensive retraining for each new dataset. We address these limitations with GRAIL, introducing a paradigm shift in this domain. Instead of training a neural model to predict GED, GRAIL employs a novel combination of large language models (LLMs) and automated prompt tuning to generate a program that is used to compute GED. This shift from predicting GED to generating programs imparts various advantages, including end-to-end interpretability and an autonomous self-evolutionary learning mechanism without ground-truth supervision. Extensive experiments on seven datasets confirm that GRAIL not only surpasses state-of-the-art GED approximation methods in prediction quality but also achieves robust cross-domain generalization across diverse graph distributions.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 43