Uncertainty quantification has become an important factor in understanding the data representations produced by Graph Neural Networks (GNNs). Despite their predictive capabilities being ever useful across industrial workspaces, the inherent uncertainty induced by the nature of the data is a huge mitigating factor to GNN performance. While aleatoric uncertainty is the result of noisy and incomplete stochastic data such as missing edges or over-smoothing, epistemic uncertainty arises from lack of knowledge about a system or model (e.g., a graph's topology or node feature representation), which can be reduced by gathering more data and information. In this paper, we propose an original new framework in which node-level epistemic uncertainty is modelled in a belief function (finite random set) formalism. The resulting Random-Set Graph Neural Networks have a belief-function head predicting a random set over the list of classes, from which both a precise probability prediction and a measure of epistemic uncertainty can be obtained. Extensive experiments on 9 different graph learning datasets, including real-world autonomous driving benchmarks as such Nuscene and ROAD, demonstrate RS-GNN's superior uncertainty quantification capabilities

翻译：不确定性量化已成为理解图神经网络（GNNs）生成的数据表征的重要因素。尽管GNNs的预测能力在工业工作空间中始终具有实用价值，但数据本身固有性质所引发的不确定性，却是限制其性能的巨大阻碍。偶然不确定性源于嘈杂且不完整的随机数据（如缺失边或过平滑），而认知不确定性则源于对系统或模型（例如图的拓扑结构或节点特征表示）缺乏认知，这种不确定性可通过收集更多数据和信息来降低。本文提出一种原创性新框架，在该框架中，节点级别的认知不确定性通过信念函数（有限随机集）形式进行建模。由此产生的随机集图神经网络配备了一个信念函数预测头，用于在类别列表上预测一个随机集，从而可以同时获得精确的概率预测和认知不确定性度量。在9个不同图学习数据集（包括如NuScenes和ROAD等真实世界自动驾驶基准）上的大量实验表明，RS-GNN具有优越的不确定性量化能力。