Exploitation of a Latent Mechanism in Graph Contrastive Learning: Representation Scattering Dongxiao He

Neural Information Processing Systems 

Graph Contrastive Learning (GCL) has emerged as a powerful approach for generating graph representations without the need for manual annotation. Most advanced GCL methods fall into three main frameworks: node discrimination, group discrimination, and bootstrapping schemes, all of which achieve comparable performance. However, the underlying mechanisms and factors that contribute to their effectiveness are not yet fully understood.

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