Uncertainty quantification on graph learning

Chao CHEN; Chenghua GUO; Rui XU; Cheng YANG; Xiangwen LIAO; Xi ZHANG; Sihong XIE; Hui XIONG; Philip S. YU

doi:10.1007/s11704-026-51464-7

Front. Comput. Sci. ›› 2027, Vol. 21 ›› Issue (5) :2105335 DOI: 10.1007/s11704-026-51464-7

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Uncertainty quantification on graph learning

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Chao CHEN, Chenghua GUO, Rui XU, Cheng YANG, Xiangwen LIAO, Xi ZHANG, Sihong XIE, Hui XIONG, Philip S. YU. Uncertainty quantification on graph learning. Front. Comput. Sci., 2027, 21(5): 2105335 DOI:10.1007/s11704-026-51464-7

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References

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[1]	Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M . Graph neural networks: A review of methods and applications. AI Open, 2020, 1: 57–81

[2]	Liu J, Yang C, Lu Z, Chen J, Li Y, Zhang M, Bai T, Fang Y, Sun L, Yu P S, Shi C . Graph foundation models: Concepts, opportunities and challenges. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, 47( 6): 5023–5044

[3]	Chen C, Guo C, Xu R, Liao X, Zhang X, Xie S, Xiong H, Yu P. Uncertainty quantification on graph learning: A survey. 2024, arXiv preprint arXiv: 2404.14642

[4]	Der Kiureghian A, Ditlevsen O. Aleatory or epistemic? Does it matter? Structural Safety, 2009, 31(2): 105−112

[5]	Xie M, Han Z, Zhang C, Bai Y, Hu Q. Exploring and exploiting uncertainty for incomplete multi-view classification. In: Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023, 19873−19882

[6]	Zhang Y, Pal S, Coates M, Üstebay D. Bayesian graph convolutional neural networks for semi-supervised classification. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 2019, 5829−5836

[7]	Liu J, Shen Z, He Y, Zhang X, Xu R, Yu H, Cui P. Towards out-of-distribution generalization: A survey. 2021, arXiv preprint arXiv: 2108.13624

[8]	Kang J, Zhou Q, Tong H. JuryGCN: quantifying jackknife uncertainty on graph convolutional networks. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022, 742−752

[9]	Wang M, Yang H, Cheng Q. GCL: Graph calibration loss for trustworthy graph neural network. In: Proceedings of the 30th ACM International Conference on Multimedia. 2022, 988−996