SEGMN: A Structure-Enhanced Graph Matching Network for Graph Similarity Learning

• URL: http://arxiv.org/abs/2411.03624v1
• Date: Wed, 06 Nov 2024 02:45:16 GMT
• Title: SEGMN: A Structure-Enhanced Graph Matching Network for Graph Similarity Learning
• Authors: Wenjun Wang, Jiacheng Lu, Kejia Chen, Zheng Liu, Shilong Sang,
• Abstract summary: Graph similarity computation (GSC) aims to quantify the similarity score between two graphs. We propose a structure-enhanced graph matching network (SEGMN) The dual embedding learning module incorporates adjacent edge representation into each node to achieve a structure-enhanced representation. The structure perception matching module achieves cross-graph structure enhancement through assignment graph convolution.
• Score: 4.506862318909861
• License:
• Abstract: Graph similarity computation (GSC) aims to quantify the similarity score between two graphs. Although recent GSC methods based on graph neural networks (GNNs) take advantage of intra-graph structures in message passing, few of them fully utilize the structures presented by edges to boost the representation of their connected nodes. Moreover, previous cross-graph node embedding matching lacks the perception of the overall structure of the graph pair, due to the fact that the node representations from GNNs are confined to the intra-graph structure, causing the unreasonable similarity score. Intuitively, the cross-graph structure represented in the assignment graph is helpful to rectify the inappropriate matching. Therefore, we propose a structure-enhanced graph matching network (SEGMN). Equipped with a dual embedding learning module and a structure perception matching module, SEGMN achieves structure enhancement in both embedding learning and cross-graph matching. The dual embedding learning module incorporates adjacent edge representation into each node to achieve a structure-enhanced representation. The structure perception matching module achieves cross-graph structure enhancement through assignment graph convolution. The similarity score of each cross-graph node pair can be rectified by aggregating messages from structurally relevant node pairs. Experimental results on benchmark datasets demonstrate that SEGMN outperforms the state-of-the-art GSC methods in the GED regression task, and the structure perception matching module is plug-and-play, which can further improve the performance of the baselines by up to 25%.

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