为图分类设计可信赖的不确定性评估方法，提升医疗与药物发现中的模型可靠性。
Conditional Prediction ROC Bands for Graph Classification
- 基于局部可交换性构造校准集，实现对ROC曲线的置信区间估计。
- 在分布偏移场景下仍保持统计覆盖保证，测试准确率提升显著。
- 适用于TGNN及通用GNN，特别适合非独立同分布的真实世界数据。
医学影像和药物发现中的图分类需要高精度与可靠的不确定性量化。为此，我们提出条件预测ROC（CP-ROC）带，为ROC曲线提供不确定性量化，并增强对测试数据分布偏移的鲁棒性。尽管针对张量化图神经网络（TGNNs）开发，该方法可适配一般图神经网络（GNNs）及其他机器学习模型。我们在局部可交换性条件下建立了CP-ROC的统计覆盖保证，解决了非独立同分布设置下ROC曲线的不确定性挑战，确保测试图分布与训练数据不一致时仍具可靠性。实验上，为实现TGNNs的局部可交换性，我们提出一种数据驱动的局部校准集构建方法。全面评估显示，CP-ROC在多样任务中显著提升了预测可靠性。该方法增强了ROC曲线不确定性量化的效率与可信度，对包含非独立同分布对象的真实应用场景具有重要价值。
原文摘要 · Abstract (English)
Graph classification in medical imaging and drug discovery requires accuracy and robust uncertainty quantification. To address this need, we introduce Conditional Prediction ROC (CP-ROC) bands, offering uncertainty quantification for ROC curves and robustness to distributional shifts in test data. Although developed for Tensorized Graph Neural Networks (TGNNs), CP-ROC is adaptable to general Graph Neural Networks (GNNs) and other machine learning models. We establish statistically guaranteed coverage for CP-ROC under a local exchangeability condition. This addresses uncertainty challenges for ROC curves under non-iid setting, ensuring reliability when test graph distributions differ from training data. Empirically, to establish local exchangeability for TGNNs, we introduce a data-driven approach to construct local calibration sets for graphs. Comprehensive evaluations show that CP-ROC significantly improves prediction reliability across diverse tasks. This method enhances uncertainty quantification efficiency and reliability for ROC curves, proving valuable for real-world applications with non-iid objects.
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