5 papers
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…
MoLS: SNR-Guided Module-Wise Learning-Rate Calibration for Large Language Models
Ziqing Wen, Zhouyang Liu, Jiahuan Wang +5
Large language models (LLMs) contain functionally distinct modules with heterogeneous gradient statistics. Although Adam(W) adapts updates at the parameter level, selecting relativ…
Hierarchy-Aware Neural Subgraph Matching with Enhanced Similarity Measure
Zhouyang Liu, Ning Liu, Yixin Chen +3
Subgraph matching is challenging as it necessitates time-consuming combinatorial searches. Recent Graph Neural Network (GNN)-based approaches address this issue by employing GNN en…
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…
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-…