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.
DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional Network
University of Science and Technology of China（中国科学技术大学）；School of Information and Artificial Intelligence, Anhui Agricultural University（安徽农业大学信息与人工智能学院）；Institutes of Physical Science and Information Technology, Anhui University（安徽大学物理科学与信息技术研究院）
Dept. of Signal Theory and Communications, Rey Juan Carlos University, Madrid, Spain（信号理论与通信系，雷亚尔·卡洛斯大学，马德里，西班牙）；Dept. of Electrical and Computer Engineering, University of Rochester（电气与计算机工程系，罗切斯特大学）
Inferred global dense residue transition graphs from primary structure sequences enable protein interaction prediction via directed graph convolutional neural networks
Division of Computer Science Texas Woman’s University（计算机科学系德克萨斯女子大学）；Department of Computer Science The University of Texas Rio Grande Valley（计算机科学系德克萨斯大学里奥格兰德谷分校）
