Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources such as inherent randomness in data and model training errors can lead to unstable and erroneous predictions. Therefore, identifying, quantifying, and utilizing uncertainty are essential to enhance the performance of the model for the downstream tasks as well as the reliability of the GNN predictions. This survey aims to provide a comprehensive overview of the GNNs from the perspective of uncertainty with an emphasis on its integration in graph learning. We compare and summarize existing graph uncertainty theory and methods, alongside the corresponding downstream tasks. Thereby, we bridge the gap between theory and practice, meanwhile connecting different GNN communities. Moreover, our work provides valuable insights into promising directions in this field.

翻译：图神经网络已广泛应用于各类实际场景。然而，源自数据固有随机性与模型训练误差等多重来源的预测不确定性，可能导致模型产生不稳定且错误的预测结果。因此，识别、量化并利用不确定性对于提升下游任务模型性能及图神经网络预测可靠性至关重要。本综述旨在从不确定性视角对图神经网络进行系统性梳理，重点关注其与图学习的融合。我们比较并总结了现有图不确定性理论与方法，及其对应的下游任务。由此桥接理论与实践之间的鸿沟，同时联结不同图神经网络研究社区。此外，本文为该领域具有前景的研究方向提供了重要见解。