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Hierarchical Graph Matching Networks for Deep Graph Similarity Learning

2020-07-08 19:48:19
Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji

Abstract

While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to deep graph similarity learning. Recent work has considered either global-level graph-graph interactions or low-level node-node interactions, ignoring the rich cross-level interactions (e.g., between nodes and a whole graph). In this paper, we propose a Hierarchical Graph Matching Network (HGMN) for computing the graph similarity between any pair of graph-structured objects. Our model jointly learns graph representations and a graph matching metric function for computing graph similarities in an end-to-end fashion. The proposed HGMN model consists of a node-graph matching network for effectively learning cross-level interactions between nodes of a graph and a whole graph, and a siamese graph neural network for learning global-level interactions between two graphs. Our comprehensive experiments demonstrate that HGMN consistently outperforms state-of-the-art graph matching network baselines for both classification and regression tasks.

Abstract (translated)

URL

https://arxiv.org/abs/2007.04395

PDF

https://arxiv.org/pdf/2007.04395.pdf


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