论文标题
定向图卷积网络
Directed Graph Convolutional Network
论文作者
论文摘要
图形卷积网络（GCN）由于其在处理图形结构数据方面的出色性能而被广泛使用。但是，无向图限制了其应用程序范围。在本文中，我们通过使用一阶和二阶接近度将基于光谱的图卷积扩展到有向图，这不仅可以保留有向图的连接属性，还可以扩展卷积操作的接受场。然后设计出一种称为DGCN的新型GCN模型，旨在在有向图上学习表示形式，利用一阶接近信息。我们从经验上表明，仅与DGCN一起工作的GCN可以从图形上编码更多有用的信息，并在推广到其他模型时帮助获得更好的性能。此外，引用网络和共同购买数据集的广泛实验证明了我们的模型与最新方法的优势。
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.
