<ul><li>Graph Contrastive Learning (GCL) focuses on aligning positive pairs and separating negative ones to learn node representations.</li><li>This paper addresses the connection between augmentation and downstream performance in GCL.</li><li>Findings reveal that GCL mainly contributes to downstream tasks by separating different classes.</li><li>Perfect alignment and augmentation overlap may not lead to the best downstream performance, so specifically designed augmentations are needed.</li></ul>

Perfect Alignment May be Poisonous to Graph Contrastive Learning

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