提升图神经网络校准效果，用组合温度缩放方法减少误判风险。
GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks
- 融合输入与模型集成，利用图结构优化校准过程。
- 在10个基准数据集上使期望校准误差降低25%。
- 计算高效且可自动选择最优输入组合，适合高风险应用。
图神经网络虽具备强大分类能力，但常因校准性能差导致过度自信或低估不确定性，这在高风险场景中尤为危险。现有后处理方法如温度缩放未能有效利用图结构，而当前GNN校准方法也忽视了多元输入信息与模型集成的协同潜力。本文提出图集成温度缩放（GETS），一种新校准框架，将输入与模型集成策略结合于图混合专家架构中。在10个GNN基准数据集上，GETS显著优于现有SOTA校准技术，期望校准误差平均降低25%。此外，GETS计算高效、可扩展，并能自动选择有效输入组合以进一步提升校准表现。代码已开源。
原文摘要 · Abstract (English)
Graph Neural Networks deliver strong classification results but often suffer from poor calibration performance, leading to overconfidence or underconfidence. This is particularly problematic in high stakes applications where accurate uncertainty estimates are essential. Existing post hoc methods, such as temperature scaling, fail to effectively utilize graph structures, while current GNN calibration methods often overlook the potential of leveraging diverse input information and model ensembles jointly. In the paper, we propose Graph Ensemble Temperature Scaling, a novel calibration framework that combines input and model ensemble strategies within a Graph Mixture of Experts archi SOTA calibration techniques, reducing expected calibration error by 25 percent across 10 GNN benchmark datasets. Additionally, GETS is computationally efficient, scalable, and capable of selecting effective input combinations for improved calibration performance. The implementation is available via Github.
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