Efficient Graph Similarity Computation with Alignment Regularization

• URL: http://arxiv.org/abs/2406.14929v1
• Date: Fri, 21 Jun 2024 07:37:28 GMT
• Title: Efficient Graph Similarity Computation with Alignment Regularization
• Authors: Wei Zhuo, Guang Tan,
• Abstract summary: Graph similarity computation (GSC) is a learning-based prediction task using Graph Neural Networks (GNNs) We show that high-quality learning can be attained with a simple yet powerful regularization technique, which we call the Alignment Regularization (AReg) In the inference stage, the graph-level representations learned by the GNN encoder are directly used to compute the similarity score without using AReg again to speed up inference.
• Score: 7.143879014059894
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider the graph similarity computation (GSC) task based on graph edit distance (GED) estimation. State-of-the-art methods treat GSC as a learning-based prediction task using Graph Neural Networks (GNNs). To capture fine-grained interactions between pair-wise graphs, these methods mostly contain a node-level matching module in the end-to-end learning pipeline, which causes high computational costs in both the training and inference stages. We show that the expensive node-to-node matching module is not necessary for GSC, and high-quality learning can be attained with a simple yet powerful regularization technique, which we call the Alignment Regularization (AReg). In the training stage, the AReg term imposes a node-graph correspondence constraint on the GNN encoder. In the inference stage, the graph-level representations learned by the GNN encoder are directly used to compute the similarity score without using AReg again to speed up inference. We further propose a multi-scale GED discriminator to enhance the expressive ability of the learned representations. Extensive experiments on real-world datasets demonstrate the effectiveness, efficiency and transferability of our approach.

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