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cs.LG2026
Revisiting the Generalization of Neural Graph Edit Distance Models
Zhouyang Liu, Ning Liu, Yixin Chen +2
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 ac…
cs.LG2025
Graph2Region: Efficient Graph Similarity Learning with Structure and Scale Restoration
Zhouyang Liu, Yixin Chen, Ning Liu +2
Graph similarity is critical in graph-related tasks such as graph retrieval, where metrics like maximum common subgraph (MCS) and graph edit distance (GED) are commonly used. Howev…
cs.LG2025
Rethinking Flexible Graph Similarity Computation: One-step Alignment with Global Guidance
Zhouyang Liu, Ning Liu, Yixin Chen +3
Graph Edit Distance (GED) is a widely used measure of graph similarity, valued for its flexibility in encoding domain knowledge through operation costs. However, existing learning-…