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

TL;DR

This paper introduces a shallow graph convolutional network enhanced with biaffine mapping to directly learn dependencies on distant nodes, overcoming the limitations of traditional shallow and deep GCNs.

Contribution

It proposes a novel biaffine technique for GCNs to efficiently capture long-distance dependencies with shallow architectures, along with a multi-view contrastive learning method.

Findings

Outperforms state-of-the-art GCNs on nine benchmark datasets.

Effectively captures long-distance dependencies with shallow architecture.

Maintains performance across different training data sizes.

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.