Graph Neural Networks (GNNs) are able to achieve high classification accuracy on many important real world datasets, but provide no rigorous notion of predictive uncertainty. Quantifying the confidence of GNN models is difficult due to the dependence between datapoints induced by the graph structure. We leverage recent advances in conformal prediction to construct prediction sets for node classification in inductive learning scenarios. We do this by taking an existing approach for conformal classification that relies on \textit{exchangeable} data and modifying it by appropriately weighting the conformal scores to reflect the network structure. We show through experiments on standard benchmark datasets using popular GNN models that our approach provides tighter and better calibrated prediction sets than a naive application of conformal prediction.

翻译：图神经网络（GNN）能在许多重要的真实世界数据集上实现高分类准确率，但无法提供严格的预测不确定性度量。由于图结构导致数据点之间存在依赖关系，量化GNN模型的置信度具有一定难度。我们利用共形预测领域的最新进展，为归纳学习场景中的节点分类构建预测集。具体方法是将现有依赖\textit{可交换}数据的共形分类方法进行改进，通过适当加权共形分数以反映网络结构。基于标准基准数据集并使用主流GNN模型的实验表明，与直接应用共形预测相比，我们的方法能生成更紧凑且校准更优的预测集。