Title: Substructured Graph Convolution for Non-overlapping Graph Decomposition. Abstract: Graph convolutional networks have been widely used to solve the graph problems such as node classification, link prediction, and recommender systems. It is well known that large graphs require large amount of memory and time to train graph convolutional networks. To deal with large graphs, many methods are being done, such as graph sampling or decomposition. In particular, graph decomposition has the advantage of parallel computation, but information loss occurs in the interface part. In this paper, we propose a novel substructured graph convolution that reinforces the interface part lost by graph decomposition. Numerical results indicate that the proposed method is robust in the number of subgraphs compared to other methods.