Uncertainty Quantification on Graph Learning: A Survey

[2404.14642] Uncertainty Quantification on Graph Learning: A Survey Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2404.14642 (cs) [Submitted on 23 Apr 2024 ( v1 ), last revised 14 Apr 2026 (this version, v4)] Title: Uncertainty Quantification on Graph Learning: A Survey Authors: Chao Chen , Chenghua Guo , Rui Xu , Jiujiu Chen , Xiangwen Liao , Xi Zhang , Sihong Xie , Hui Xiong , Philip Yu View a PDF of the paper titled Uncertainty Quantification on Graph Learning: A Survey, by Chao Chen and 8 other authors View PDF HTML (experimental) Abstract: Graphical models have demonstrated their exceptional capabilities across numerous applications. However, their performance, confidence, and trustworthiness are often limited by the inherent randomness in data generation and the lack of knowledge to accurately model real-world complexities. There has been increased interest in developing uncertainty quantification (UQ) techniques tailored to graphical models. In this survey, we systematically examine existing works on UQ for graphical models. This survey distinguishes itself from most existing UQ surveys by specifically concentrating on graphical models, including graph neural networks and graph foundation models. We organize the literature along two complementary dimensions: uncertainty representation and uncertainty handling. By synthesizing both established methodologies and emerging trends, we aim to bridge gaps in understanding key challenges and opportunities in UQ for graphical models, inspiring researchers on graphical models or uncertainty quantification to make further advancements at the cross of the two fields. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2404.14642 [cs.LG] (or arXiv:2404.14642v4 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2404.14642 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Chao Chen [ view email ] [v1] Tue, 23 Apr 2024 00:39:26 UTC (1,310 KB) [v2] Fri, 20 Sep 2024 15:18:50 UTC (1,328 KB) [v3] Tue, 24 Jun 2025 10:02:19 UTC (3,979 KB) [v4] Tue, 14 Apr 2026 02:53:06 UTC (533 KB) Full-text links: Access Paper: View a PDF of the paper titled Uncertainty Quantification on Graph Learning: A Survey, by Chao Chen and 8 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2024-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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