Revisiting the Generalization of Neural Graph Edit Distance Models

Abstract:Neural approaches to Graph Edit Distance (GED) have achieved strong results under standard within-dataset evaluation, but much less is known about how well these models transfer across graph collections. We conduct a systematic study of this problem using exact GED supervision across diverse graph datasets and a broad set of representative learning-based methods. Our results reveal a pronounced gap between within-collection performance and cross-collection transfer. Models that perform well on their training collections often lose this advantage when evaluated on structurally different data. Training on multiple source collections substantially improves zero-shot transfer and provides a better starting point when limited supervision is available for a new target collection. Further analysis shows that transfer behavior varies with the source--target direction and the structural characteristics of the collections involved. These findings suggest that conventional within-collection evaluation provides only a partial view of the generalization behavior of neural GED models and motivate broader evaluation across heterogeneous graph collections.

Machine Learning (cs.LG)

arXiv:2610.04644 [cs.LG]

(or arXiv:2610.04644v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.04644 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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