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（GNNs）已在众多现实应用中广泛使用。然而，GNNs的预测不确定性源于多种因素，如数据固有的随机性和模型训练误差，可能导致不稳定甚至错误的预测结果。因此，识别、量化并利用不确定性对于提升模型在下游任务中的性能以及增强GNN预测的可靠性至关重要。本综述旨在从不确定性的视角，对图神经网络进行全面概述，并着重探讨其在图学习中的融合应用。我们比较并总结了现有的图不确定性理论与方法，以及相应的下游任务。借此，我们弥合了理论与实践之间的鸿沟，同时连接了不同的GNN研究群体。此外，我们的工作为该领域未来有前景的研究方向提供了有价值的见解。