Efficient Graph Similarity Computation with Alignment Regularization
NeurIPS(2024)
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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