发表机构
National University of Defense Technology; Information Support Force Engineering University（国防科技大学; 信息支援部队工程大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究系统评估了神经图编辑距离模型的跨集合迁移能力，发现同集合评估高估了泛化性能，多源训练可显著提升零样本迁移，并建议采用异构集合进行更全面的评估。
AI 中文摘要
神经图编辑距离（GED）方法在标准的同数据集评估中取得了显著成果，但关于这些模型在不同图集合之间的迁移表现，目前了解甚少。我们利用精确的GED监督信号，在多样化的图数据集和一系列具有代表性的基于学习方法上，对该问题进行了系统性研究。研究结果揭示了同集合性能与跨集合迁移之间的显著差距。在训练集合上表现良好的模型，在评估结构不同的数据时往往失去这一优势。在多个源集合上进行训练能大幅提升零样本迁移性能，并在新目标集合监督信号有限时提供更好的起点。进一步分析表明，迁移行为随源-目标方向及所涉及集合的结构特征而变化。这些发现表明，传统的同集合评估仅能部分反映神经GED模型的泛化行为，并促使我们在异构图集合上进行更广泛的评估。
英文摘要
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.
