Take Attention Inside: Neighbor Pair Graph Contrastive Learning

Abstract

Graph Contrastive Learning(GCL) is a fundamental pretraining research method in Graph Neural Networks (GNNs), which puts rich graph-level insights into the graph data to augment the data representation. However, since the existing GCLs generally regard the intra-layer node as negative samples, they cannot cope with the diverse coupled neighbor relationships, which can be pre-trained with the combination of negative and positive samples. Coupled relationships can keep the attribute preference in node-level contrast and correctly pass this preference into the downstream tasks. To further prove the effectiveness of coupled neighbor relationships in the pretraining phase, we propose a novel GNN pretraining model Neighbor Pair Contrastive Graph Siamese Networks (NPC-GSN) for graph contrast. NPC-GSN expands the dissimilar neighbor’s representation discrepancy and decreases the representation discrepancy of similar neighbors in the pretraining phase, aiming to promote downstream node classification. Our extensive experiments on five graph datasets against several pretraining GNN models demonstrate the competitive effectiveness of NPC-GSN in node classification, and the frequency domain and ablation experiments also verify the effectiveness of NPC-GSN.