AI RESEARCH
Adaptive Negative Scheduling for Graph Contrastive Learning
arXiv CS.LG
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ArXi:2605.03076v1 Announce Type: new Graph contrastive learning (GCL) has become a central paradigm for self-supervised representation learning in computational intelligence, with applications spanning recommendation, anomaly detection, and personalization. A key limitation of existing methods is their reliance on static negative sampling, which fails to account for the dynamic informativeness and computational cost of negatives during