分布偏移何时会破坏图神经网络的校准？

中文摘要 AI 辅助

图神经网络（GNN）越来越多地应用于不可避免会出现分布偏移的实际场景中。然而，这种偏移如何影响模型校准（即预测置信度与实际准确率之间的一致性）却仍未得到充分理解，且现有图校准方法通常依赖于来自部署分布的带标签验证数据。在这项工作中，作者给出了分布偏移下GNN校准的首个闭式理论表征。表明校准由一个明确依赖于源图和目标图之间结构变化以及特征质量的标量控制。此表征精确确定模型何时过度自信、信心不足或保持校准，并直接得出最优温度缩放策略。作者还将分析扩展到具有对称归一化、多类分类和协变量偏移的图卷积网络，并推导了预期校准误差的理论上限。分析还揭示，在均匀分布偏移下，单个全局温度在理论上是最优的，为更复杂的节点级重新校准方法为何无额外益处提供了原理性解释。基于这些理论见解，作者提出了无源、无标签的校准方法STAC。在合成基准上的实验证明校准有显著改进，而在五个真实世界图数据集上的评估表明，尽管理论有强大预测能力，但无目标标签的可靠校准仍具有挑战性。

英文摘要

Graph neural networks (GNNs) are increasingly deployed in real-world applications where distribution shift is un-avoidable. However, how such shifts affect model calibration, defined as the agreement between predictive confidence and actual accuracy, remains poorly understood, and existing graph calibration methods typically rely on labeled validation data from the deployment distribution. In this work, I present the first closed-form theoretical characterization of GNN calibration under distribution shift. I show that calibration is governed by a single scalar quantity that explicitly depends on structural changes between the source and target graphs, as well as feature quality. This characterization precisely identifies when a model becomes over-confident, under-confident, or remains calibrated, and directly yields the optimal temperature scaling strategy. I further extend the analysis to graph convolutional networks with symmetric normalization, multi-class classification, and covariate shift, and derive a theoretical upper bound on the expected calibration error. My analysis also reveals that, under homogeneous distribution shift, a single global temperature is theoretically optimal, providing a principled explanation for why more complex node-wise recalibration methods offer no additional benefit. Building on these theoretical insights, I propose STAC, a source-free, label-free calibration method. Experiments on synthetic benchmarks demonstrate substantial calibration improvements, while evaluations on five real-world graph datasets show that reliable calibration without target labels remains challenging despite the strong predictive power of the theory.

发表机构

• Computer Science and Applied Mathematics Laboratory (LIMA), Faculty of Science and Technology, Chadli Bendjedid University（计算机科学与应用数学实验室（LIMA），科学与技术学院，查德利·本杰迪德大学）

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