用排序机制提升GNN的可信度，让预测结果更可靠且高效。
Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training
- 基于分类器输出的排序信息构建预测集，保证覆盖率
- 训练时引入可微分的排序损失，减少无效预测集
- 适合对可靠性要求高的图数据场景，如医疗诊断
图神经网络（GNN）在表示图结构数据方面潜力巨大，但缺乏严格的不确定性估计限制了其在高风险领域的应用。合规预测（CP）可通过分类器的概率输出生成具有统计保障的不确定性估计，即预测集以用户指定概率包含真实类别。本文提出一种训练阶段的基于排序的合规预测框架（RCP-GNN），用于节点分类任务中实现可靠的不确定性估计，以增强GNN的可信度。通过利用分类器输出的排序信息，可高效构造满足目标覆盖率的预测集。进一步探索了带有可微分排序型符合性损失函数的训练策略，使预测集能根据网络拓扑信息自适应调整，从而联合优化无效性和概率估计误差。在多个真实世界数据集上的实验表明，所提方法可在任意预设目标边际覆盖率下显著降低无效性，优于现有最先进方法。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) has been widely used in a variety of fields because of their great potential in representing graph-structured data. However, lacking of rigorous uncertainty estimations limits their application in high-stakes. Conformal Prediction (CP) can produce statistically guaranteed uncertainty estimates by using the classifier's probability estimates to obtain prediction sets, which contains the true class with a user-specified probability. In this paper, we propose a Rank-based CP during training framework to GNNs (RCP-GNN) for reliable uncertainty estimates to enhance the trustworthiness of GNNs in the node classification scenario. By exploiting rank information of the classifier's outcome, prediction sets with desired coverage rate can be efficiently constructed. The strategy of CP during training with differentiable rank-based conformity loss function is further explored to adapt prediction sets according to network topology information. In this way, the composition of prediction sets can be guided by the goal of jointly reducing inefficiency and probability estimation errors. Extensive experiments on several real-world datasets show that our model achieves any pre-defined target marginal coverage while significantly reducing the inefficiency compared with state-of-the-art methods.
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