# Revisiting the Generalization of Neural Graph Edit Distance Models

arXiv: https://arxiv.org/abs/2610.04644 · HTML: https://mlsanity.com/paper/2610.04644/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 3 Oct 2026.

## Builds on (most cited first, 20 of 20)

- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [Graph Matching Networks for Learning the Similarity of Graph Structured Objects](https://mlsanity.com/paper/1904.12787/) (2019)
- [SimGNN: A Neural Network Approach to Fast Graph Similarity Computation](https://mlsanity.com/paper/1808.05689/) (2018)
- [Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching](https://mlsanity.com/paper/1809.04440/) (2018)
- [GREED: A Neural Framework for Learning Graph Distance Functions](https://mlsanity.com/paper/2112.13143/) (2021)
- [Efficient Graph Similarity Computation with Alignment Regularization](https://mlsanity.com/paper/2406.14929/) (2024)
- [Combinatorial Learning of Graph Edit Distance via Dynamic Embedding](https://mlsanity.com/paper/2011.15039/) (2020)
- [When to Pre-Train Graph Neural Networks? From Data Generation Perspective!](https://mlsanity.com/paper/2303.16458/) (2023)
- [Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning](https://mlsanity.com/paper/2104.05960/) (2021)
- [Graph Edit Distance with General Costs Using Neural Set Divergence](https://mlsanity.com/paper/2409.17687/) (2024)
- [MATA*: Combining Learnable Node Matching with A* Algorithm for Approximate Graph Edit Distance Computation](https://mlsanity.com/paper/2311.02356/) (2023)
- [GRAIL: Graph Edit Distance and Node Alignment Using LLM-Generated Code](https://mlsanity.com/paper/2505.02124/) (2025)
- [Computing Approximate Graph Edit Distance via Optimal Transport](https://mlsanity.com/paper/2412.18857/) (2024)
- [GraSP: Simple yet Effective Graph Similarity Predictions](https://mlsanity.com/paper/2412.09968/) (2024)
- [GraphBridge: Towards Arbitrary Transfer Learning in GNNs](https://mlsanity.com/paper/2502.19252/) (2025)
- [Towards Unsupervised Training of Matching-based Graph Edit Distance Solver via Preference-aware GAN](https://mlsanity.com/paper/2506.01977/) (2025)
- [Rethinking Flexible Graph Similarity Computation: One-step Alignment with Global Guidance](https://mlsanity.com/paper/2504.06533/) (2025)
- [Graph2Region: Efficient Graph Similarity Learning with Structure and Scale Restoration](https://mlsanity.com/paper/2510.00394/) (2025)
- [Hierarchy-Aware Neural Subgraph Matching with Enhanced Similarity Measure](https://mlsanity.com/paper/2510.00402/) (2025)

## Abstract (arXiv)

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.