  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.
