让图神经网络的预测更可靠，精准量化不确定性。
GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs
- 基于图结构设计局部化预测方法，融合节点间依赖关系
- 在多个数据集上实现有限样本下可靠的覆盖率和高效预测集
- 适合需要可信预测的图数据场景，如医疗或社交网络分析
置信预测（CP）提供了一种无需分布假设的不确定性量化方法，并具备有限样本保证。然而，将CP应用于图神经网络（GNNs）仍具挑战性，因图的组合特性常导致预测不确定性不足且嵌入表示缺乏区分性。现有方法主要依赖嵌入空间中的邻近性进行局部化，但对图结构不敏感，且预测集效率低。本文提出GRAPHLCP，一种基于邻近性的局部化置信预测框架，显式地将图拓扑和节点间依赖关系融入局部化与加权过程。该方法引入特征感知的密度增强步骤，缓解稀疏图中的局部偏差，再通过个性化PageRank计算结构邻近性核，实现依赖图结构的锚点采样与校准加权，从而捕捉局部与长程依赖。在多个回归与分类数据集上的大量实验表明，GRAPHLCP在有限样本下保证了边际覆盖率，并在各种条件场景下实现了良好的测试条件覆盖率。
原文摘要 · Abstract (English)
Conformal prediction (CP) provides a distribution-free approach to uncertainty quantification with finite-sample guarantees. However, applying CP to graph neural networks (GNNs) remains challenging as the combinatorial nature of graphs often leads to insufficiently certain predictions and indiscriminative embeddings. Existing methods primarily rely on embedding-space proximity for localization, which can be unreliable for graphs and yield inefficient prediction sets. We propose GRAPHLCP, a proximity-based localized CP framework that explicitly incorporates graph topology and inter-node dependencies into localization and weighting. Our approach introduces a feature-aware densification step to mitigate locality bias in sparse graphs, followed by a Personalized PageRank-based kernel computation to model structural proximity. This enables topology-dependent anchor sampling and calibration weighting that captures both local and long-range dependencies. Extensive experiments on several regression and classification datasets demonstrate that GRAPHLCP guarantees marginal coverage with finite samples while efficiently attaining favorable test conditional coverage across various conditioning scenarios.
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