Graph Convolutional Networks (GCNs) have attracted more and more attentions
in recent years. A typical GCN layer consists of a linear feature propagation
step and a nonlinear transformation step. Recent works show that a linear GCN
can achieve comparable performance to the original non-linear GCN while being
much more computationally efficient. In this paper, we dissect the feature
propagation steps of linear GCNs from a perspective of continuous graph
diffusion, and analyze why linear GCNs fail to benefit from more propagation
steps. Following that, we propose Decoupled Graph Convolution (DGC) that
decouples the terminal time and the feature propagation steps, making it more
flexible and capable of exploiting a very large number of feature propagation
steps. Experiments demonstrate that our proposed DGC improves linear GCNs by a
large margin and makes them competitive with many modern variants of non-linear
GCNs.

通过对连续图扩散的角度解剖了线性 GCNs 的特征传播步骤，分析了为什么线性 GCN 不能从更多的传播步骤中受益。随后，我们提出解耦的图卷积（DGC），分离终端时间和特征传播步骤，使其更灵活，能够利用非常多的特征传播步骤。实验表明，我们提出的 DGC 显著改善了线性 GCNs，并使它们能够与许多现代非线性 GCNs 的变体竞争。