Teleport Graph Convolutional Networks

We consider the limitations in message-passing graph neural networks. In message-passing operations, each node aggregates information from its neighboring nodes. To enlarge the receptive field, graph neural networks need to stack multiple message-passing graph convolution layers, which leads to the over-fitting issue and over-smoothing issue. To address these limitations, we propose a teleport graph convolution layer (TeleGCL) that uses teleport functions to enable each node to aggregate information from a much larger neighborhood. For each node, teleport functions select relevant nodes beyond the local neighborhood, thereby resulting in a larger receptive field. To apply our structure-aware teleport function, we propose a novel method to construct structural features for nodes in the graph. Based on our TeleGCL, we build a family of teleport graph convolutional networks. The empirical results on graph and node classification tasks demonstrate the effectiveness of our proposed methods.