AFECL learns graph representations by contrasting edges that share a node against all other edges, without data augmentation, and reports state-of-the-art results on low-label node classification and link prediction.
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Edge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning Model
AFECL learns graph representations by contrasting edges that share a node against all other edges, without data augmentation, and reports state-of-the-art results on low-label node classification and link prediction.