Select Your Own Counterparts: Self-Supervised Graph Contrastive Learning With Positive Sampling

Contrastive learning (CL) has emerged as a powerful approach for self-supervised learning. However, it suffers from sampling bias, which hinders its performance. While the mainstream solutions, hard negative mining (HNM) and supervised CL (SCL), have been proposed to mitigate this critical issue, they do not effectively address graph CL (GCL). To address it, we propose graph positive sampling (GPS) and three contrastive objectives. The former is a novel learning paradigm designed to leverage the inherent properties of graphs for improved GCL models, which utilizes four complementary similarity measurements, including node centrality, topological distance, neighborhood overlapping, and semantic distance, to select positive counterparts for each node. Notably, GPS operates without relying on true labels and enables preprocessing applications. The latter aims to fuse positive samples and enhance representative selection in the semantic space. We release three node-level models with GPS and conduct extensive experiments on public datasets. The results demonstrate the superiority of GPS over state-of-the-art (SOTA) baselines and debiasing methods. In addition, the GPS has also been proven to be versatile, adaptive, and flexible.