Graph contrastive learning with node-level accurate difference
Graph contrastive learning (GCL) has attracted extensive research interest due to its powerful ability to capture latent structural and semantic information of graphs in a self-supervised manner.Existing GCL methods commonly adopt predefined graph augmentations to generate two contrastive views.Subsequently, they design a contrastive pretext task b