Graph Convolutional Networks (GCNs) have become a crucial tool on learning
representations of graph vertices. The main challenge of adapting GCNs on
large-scale graphs is the scalability issue that it incurs heavy cost both in
computation and memory due to the uncontrollable neighborhood expansion across
layers. In this paper, we accelerate the training of GCNs through developing an
adaptive layer-wise sampling method. By constructing the network layer by layer
in a top-down passway, we sample the lower layer conditioned on the top one,
where the sampled neighborhoods are shared by different parent nodes and the
over expansion is avoided owing to the fixed-size sampling. More importantly,
the proposed sampler is adaptive and applicable for explicit variance
reduction, which in turn enhances the training of our method. Furthermore, we
propose a novel and economical approach to promote the message passing over
distant nodes by applying skip connections. Intensive experiments on several
benchmarks verify the effectiveness of our method regarding the classification
accuracy while enjoying faster convergence speed.

本研究提出了一种自适应逐层采样方法和跨越较远节点的消息传递方法，以解决图形卷积网络在大规模图中的可扩展性问题，并通过实验验证了其有效性和更快的收敛速度。