Uncertainty Quantification on Graph Learning: A Survey
<|reference_start|>Uncertainty Quantification on Graph Learning: A Survey: Graphical models have demonstrated their exceptional capabilities across numerous applications, such as social networks, citation networks, and online recommendation systems. Despite these successes, their performance, confidence, and trustworthiness are often limited by the inherent randomness of data in nature and the challenges of accurately capturing and modeling real-world complexities. This has increased interest in developing uncertainty quantification (UQ) techniques tailored to graphical models. In this survey, we comprehensively examine these existing works on UQ in graphical models, focusing on key aspects such as foundational knowledge, sources, representation, handling, and measurement of uncertainty. This survey distinguishes itself from most existing UQ surveys by specifically concentrating on UQ in graphical models, particularly probabilistic graphical models (PGMs) and graph neural networks (GNNs). We elaborately categorize recent work into two primary areas: uncertainty representation and uncertainty handling. By offering a comprehensive overview of the current landscape, including both established methodologies and emerging trends, we aim to bridge gaps in understanding and highlight key challenges and opportunities in the field. Through in-depth discussion of existing works and promising directions for future research, we believe this survey serves as a valuable resource for researchers, inspiring them to cope with uncertainty issues in both academic research and real-world applications.<|reference_end|>
@article{chen2024uncertainty,
    title={Uncertainty Quantification on Graph Learning: A Survey},
    author={Chao Chen, Chenghua Guo, Rui Xu, Xiangwen Liao, Xi Zhang, Sihong Xie,
  Hui Xiong, Philip Yu},
    journal={arXiv preprint arXiv:2404.14642},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14642},
    primaryClass={cs.LG}
}