Directed Graph Convolutional Network

• URL: http://arxiv.org/abs/2004.13970v1
• Date: Wed, 29 Apr 2020 06:19:10 GMT
• Title: Directed Graph Convolutional Network
• Authors: Zekun Tong, Yuxuan Liang, Changsheng Sun, David S. Rosenblum and Andrew Lim
• Abstract summary: We extend spectral-based graph convolution to directed graphs by using first- and second-order proximity. A new GCN model, called DGCN, is then designed to learn representations on the directed graph.
• Score: 15.879411956536885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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