Graph-CAT: Graph Co-Attention Networks via local and global attribute augmentations

Graph neural networks have achieved tremendous success in semi-supervised node classification. In this paper, we firstly analyse the propagation strategies in two milestone methods, Graph Convolutional Network (GCN) and Graph Attention Network (GAT), to reveal their underlying philosophies. According to our analysis, the propagations in GAT can be interpreted as learnable and asymmetric local attribute augmentations, while that of GCN can be interpreted as fixed and symmetric local attribute smoothing. Unfortunately, the local attribute augmentations in GAT is not adequate in certain circumstances, because the nodes tend to possess similar attributes in local neighbourhoods. With a toy experiment, we manage to demonstrate the necessity to incorporate global information. Therefore, we propose a novel Graph Co-ATtention Network (Graph-CAT), which performs both the local and global attribute augmentations based on two different yet complementary attention schemes. Extensive experiments in both the transductive and inductive tasks demonstrate the superiority of our Graph-CAT compared to the state-of-the-art methods.