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
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