AI RESEARCH
Less is More: Towards Simple Graph Contrastive Learning
arXiv CS.LG
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ArXi:2509.25742v3 Announce Type: replace Graph Contrastive Learning (GCL) has shown strong promise for unsupervised graph representation learning, yet its effectiveness on heterophilic graphs, where connected nodes often belong to different classes, remains limited. Most existing methods rely on complex augmentation schemes, intricate encoders, or negative sampling, which raises the question of whether such complexity is truly necessary in this challenging setting.