How Far Does Graph Edit Distance Learning Travel? A Cross-Dataset Benchmark of Neural Models

Graph Edit Distance (GED) provides a domain-agnostic measure of structural dissimilarity, yet neural GED methods are typically developed and evaluated within a single graph collection. Whether the knowledge learned from exact GED supervision transfers across collections remains largely unexplored. To study this question, we introduce an open benchmark containing 5,523 graphs and 1,903,452 exact graph pairs from 11 collections, with node alignments and a unified evaluation framework covering 20 representative learning-based methods. Our experiments reveal a strong dependence on the collections used for training. Among the evaluated methods, 18 outperform the best approximation results on their source collections, yet none retains this advantage when directly transferred to other collections. Exposing the same methods to multiple source collections substantially improves performance on unseen targets, and the resulting models also provide a better starting point when limited target supervision becomes available. These results show that conventional within-collection evaluation provides an incomplete view of neural GED generalization. We expect this benchmark to support future research on neural GED methods with stronger cross-collection generalization.