Title: Vectorial Graph Convolutional Networks. Abstract:    Graph Convolutional Networks (GCN) have drawn considerable attention recently due to their outstanding performance in processing graph-structured data. However, GCNs still limited to the undirected graph because they theoretically require a symmetric matrix as the basis for the Laplacian transform. This causes the isotropic problem of the operator and reduced sensitivity in response to different information. In order to solve the problem, we generalize the spectral convolution operator to directed graphs by field extension, which improves the edge representations from scalars to vectors. Therefore, it brings in the concept of direction. That is to say, and even homogeneous information can become distinguishable by its differences in directions.In this paper, we propose the Vectorial Graph Convolutional Network(VecGCN) and the experimental evidence showing the advantages of a variety of directed graph node classification and link prediction tasks. 