Toward graph classification on structure property using adaptive motif based on graph convolutional network

Graph convolutional network (GCN) has been widely used in handling various graph data analysis tasks. For graph classification tasks, existing work on graph classification mainly focuses on two aspects of graph similarity: physical structure and practical property. In this paper, we consider the problem of graph classification from a new perspective, namely structural properties. Graph similarity is defined based on structural properties such as maximum clique, minimum vertex coverage, and minimum dominating set of graphs. To capture these structural features, we design an adaptive motif to mine the higher-order connectivity information among nodes. Furthermore, to obtain the unique down-sampling in graph pooling stage, we propose a de-correlation pooling approach. Our extensive experiments on several artificially generated datasets show that our proposed model can effectively classify graphs with similar structural property. It is also experimentally compared with the baseline approach to demonstrate the effectiveness of our adaptive motif GCNs.