提出统一校准框架，用最后层权重衰减解决GNN置信度偏低问题。
The Final Layer Holds the Key: A Unified and Efficient GNN Calibration Framework
- 基于最终层的类中心与节点级联合校准机制
- 降低最后层权重衰减可缓解模型欠自信，提升置信度
- 无需额外模块，计算高效，适合实际部署
图神经网络（GNN）在图任务中表现优异，但其预测置信度常出现欠自信现象，影响决策可靠性。现有校准方法多引入额外组件，未能捕捉模型与置信度间的内在关系，导致理论保障不足且计算开销增加。本文提出一种简单高效的图校准方法，建立统一理论框架，揭示模型置信度由最终层的类中心级与节点级校准共同决定。理论上证明：降低最终层参数的权重衰减可从类中心层面缓解欠自信；节点级校准则使测试节点在最终表示中更靠近其预测类中心，作为细粒度补充。大量实验验证了该方法的优越性。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness on graph-based tasks. However, their predictive confidence is often miscalibrated, typically exhibiting under-confidence, which harms the reliability of their decisions. Existing calibration methods for GNNs normally introduce additional calibration components, which fail to capture the intrinsic relationship between the model and the prediction confidence, resulting in limited theoretical guarantees and increased computational overhead. To address this issue, we propose a simple yet efficient graph calibration method. We establish a unified theoretical framework revealing that model confidence is jointly governed by class-centroid-level and node-level calibration at the final layer. Based on this insight, we theoretically show that reducing the weight decay of the final-layer parameters alleviates GNN under-confidence by acting on the class-centroid level, while node-level calibration acts as a finer-grained complement to class-centroid level calibration, which encourages each test node to be closer to its predicted class centroid at the final-layer representations. Extensive experiments validate the superiority of our method.
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