AdNGCL: Adaptive Negative Scheduling for Graph Contrastive Learning
AdNGCL introduces an adaptive negative scheduling mechanism for Graph Contrastive Learning. It manages negative sampling by categorizing negatives into hard, intermediate, and easy sets and applying a budget constraint. This loss-gated approach dynamically adjusts how many negatives are used during training to maximize learning while controlling computational cost.