Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks

• URL: http://arxiv.org/abs/2302.08727v2
• Date: Mon, 20 Feb 2023 02:40:13 GMT
• Title: Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks
• Authors: Acong Zhang and Jincheng Huang and Ping Li and Kai Zhang
• Abstract summary: We introduce Biaffine technique to improve the expressiveness of graph convolutional networks with a shallow architecture. Our method is to learn direct dependency on long-distance neighbors for nodes, with which only one-hop message passing is capable of capturing rich information for node representation.
• Score: 18.160610500658183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple recent studies show a paradox in graph convolutional networks (GCNs), that is, shallow architectures limit the capability of learning information from high-order neighbors, while deep architectures suffer from over-smoothing or over-squashing. To enjoy the simplicity of shallow architectures and overcome their limits of neighborhood extension, in this work, we introduce Biaffine technique to improve the expressiveness of graph convolutional networks with a shallow architecture. The core design of our method is to learn direct dependency on long-distance neighbors for nodes, with which only one-hop message passing is capable of capturing rich information for node representation. Besides, we propose a multi-view contrastive learning method to exploit the representations learned from long-distance dependencies. Extensive experiments on nine graph benchmark datasets suggest that the shallow biaffine graph convolutional networks (BAGCN) significantly outperforms state-of-the-art GCNs (with deep or shallow architectures) on semi-supervised node classification. We further verify the effectiveness of biaffine design in node representation learning and the performance consistency on different sizes of training data.

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