Repulsion-GNNs: Use Repulsion to Supplement Aggregation

Jian Gao, Jianshe Wu

Abstract: Graph Neural Networks (GNNs) have achieved prominent performance in the node classification task, by constructing an aggregation process to integrate node features and graph topology. The aggregation process makes the features of the connected nodes similar, which helps to classify. However, this will also cause nodes that are connected but belong to different classes to be more confusing. In this paper, we propose Repulsion-GNNs, in which a repulsion process is introduced to supplement the aggregation process. First, the nodes are divided into hyper nodes based on a basic node classification model. Then, the repulsion for each node can be obtained according to these hyper nodes. Finally, the node embeddings are obtained by combining the aggregation and repulsion. Many existing GNNs can be combined with the repulsion without adding any learnable parameter. Extensive experiments on benchmark datasets for node classification demonstrate that the repulsion can boost the performance of many GNNs, such as GCN, GAT, SAGE, and GCNII.