AdNGCL: Adaptive Negative Scheduling for Graph Contrastive Learning
Negative samples play a crucial role in graph contrastive learning (GCL), yet existing methods mainly focus on which negatives should be selected, mined, filtered, or progressively introduced. Less attention has been given to separating the negative candidates available to an anchor from the subset that actively participates in optimization, and to adapting this exposure according to training behavior. To address this gap, we propose the Adaptive Negative Scheduling for Graph Contrastive Learning (AdNGCL) framework, which maintains a bounded per-anchor candidate reservoir and periodically reassesses candidate hardness as representations evolve. Its core component, Hardness-Aware Negative ...
| Authors | Adnan Ali, Jinlong Li, Syed Muhammad Israr et al. |
|---|---|
| Primary category | cs.LG |
| Published | 4 May 2026 |
| Last updated at source | 13 October 2026 |
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