Graph Convolutional Networks (GCNs) have been widely used due to their
outstanding performance in processing graph-structured data. However, the
undirected graphs limit their application scope. In this paper, we extend
spectral-based graph convolution to directed graphs by using first- and
second-order proximity, which can not only retain the connection properties of
the directed graph, but also expand the receptive field of the convolution
operation. A new GCN model, called DGCN, is then designed to learn
representations on the directed graph, leveraging both the first- and
second-order proximity information. We empirically show the fact that GCNs
working only with DGCNs can encode more useful information from graph and help
achieve better performance when generalized to other models. Moreover,
extensive experiments on citation networks and co-purchase datasets demonstrate
the superiority of our model against the state-of-the-art methods.

本研究提出了一种名为 DGCN 的新型 GCN 模型，通过利用一阶和二阶接近度来将其扩展到有向图，可以保留有向图的连接属性并扩展卷积操作的感受野，实验证明只用 DGCNs 可编码更多有用的图信息并在推广到其他模型时提高性能。