CaliGCL: Calibrated Graph Contrastive Learning via Partitioned Similarity and Consistency Discrimination

Graph contrastive learning (GCL) aims to learn self-supervised representations by distinguishing positive and negative sample pairs generated from multiple augmented graph views.