Uncertainty in Graph Neural Networks: A Survey
It provides a comprehensive overview for researchers and practitioners in graph learning, but it is incremental as it surveys existing work without introducing new methods.
This survey addresses the problem of predictive uncertainty in Graph Neural Networks (GNNs), which arises from data randomness and training errors, and aims to enhance model performance and reliability by summarizing existing theories and methods.
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.