Uncertainty in Graph Neural Networks: A Survey
Abstract: 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.
Submission Length: Long submission (more than 12 pages of main content)
Changes Since Last Submission: We have incorporated the suggestions from reviewer hKXz's reply in this revised version. The revision is indicated in red in the draft. We substituted the names of some terms to make them more consistent.
Assigned Action Editor: ~Lei_Li11
Submission Number: 2314
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