mlsanityData to 14 Oct 2026

Uncertainty Quantification on Graph Learning: A Survey

arXiv 2404.14642 ↗First posted 23 Apr 2024cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 165 of its 302 references that are in the map. First posted on arXiv 23 Apr 2024.

Builds on

165 of 302 references.

References · 165

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38 more
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50 most cited of 165 All 165 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2026Random-Set Graph Neural Networkscited by 3
  2. 2025Credal Graph Neural Networkscited by 2
  3. 2026Quantile-Free Uncertainty Quantification in Graph Neural Networkscited by 0
  4. 2026Rethinking Epistemic Uncertainty in Node Classification through Information Growthcited by 0