Adaptive Negative Scheduling for Graph Contrastive Learning

2026-05-04

AI Summary

Existing graph contrastive learning methods rely on static negative sampling, which struggles to dynamically balance informativeness and computational overhead. This work proposes AdNGCL, a novel framework that introduces, for the first time, a budget-aware, loss-sensitive Hardness-Aware Negative Scheduler (HANS). HANS formulates negative sample selection as a dynamic process governed by loss gating and computational budget constraints, adaptively adjusting sampling strides across hard, medium, and easy negatives while periodically refreshing the pool to preserve diversity. Evaluated on nine benchmark graph datasets, AdNGCL achieves state-of-the-art performance on seven and runner-up results on two, significantly improving accuracy while enabling explicit control over computational cost.

Abstract

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 training. We propose AdNGCL, an adaptive negative scheduling framework with a hardness-aware scheduler (HANS) that formulates negative selection as a loss-gated, budget-constrained process across hard, intermediate, and easy strata. The scheduler dynamically adjusts step sizes based on contrastive loss trends under both global and per-category budgets, while periodically refreshing samples to maintain diversity without exceeding compute constraints. Experiments on nine benchmark graph datasets demonstrate that AdNGCL consistently advances state-of-the-art performance, achieving the best accuracy on seven datasets and second-best on the remaining two, while offering explicit control over computational cost. These results highlight the value of budget-aware, loss-sensitive scheduling as a general strategy for improving the robustness and efficiency of representation learning in emerging computational intelligence applications.

Problem

• graph contrastive learning
• negative sampling
• computational cost
• self-supervised representation learning
• adaptive scheduling

Innovation

• adaptive negative scheduling
• graph contrastive learning
• hardness-aware scheduler
• budget-constrained sampling
• self-supervised representation learning