Improving Graph Networks Through Selection-Based Convolution

Graph Convolutional Networks (GCNs) provide a general framework that can learn in a variety of data domains, such as 3D geometry, social networks, and chemical structures. GCNs, however, often ignore intrinsic relationships among nodes in the graph, and these relationships need to be learned indirectly during the training process through mechanisms such as attention or local-kernel approximation. This paper introduces selection-based graph convolution, a method for preserving these intrinsic relationships within the graph convolution operator which provides improved performance over attention-based counterparts on various tasks. We demonstrate the effectiveness of selection to improve the performance of many types of GCNs on tasks such as spatial graph classification. Furthermore, we demonstrate the ability to improve state-of-the-art graph networks for road traffic estimation and molecular property prediction.