mlsanityData to 14 Oct 2026

Credal Graph Neural Networks

arXiv 2512.02722 ↗First posted 2 Dec 2025cs.LGcited by 2

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Abstract (arXiv)

Uncertainty quantification is essential for deploying reliable Graph Neural Networks (GNNs), where existing approaches primarily rely on Bayesian inference or ensembles. In this paper, we introduce the first credal graph neural networks (CGNNs), which extend credal learning to the graph domain by training GNNs to output set-valued predictions in the form of credal sets. To account for the distinctive nature of message passing in GNNs, we develop a complementary approach to credal learning that leverages different aspects of layer-wise information propagation. We assess our approach on uncertainty quantification in node classification under out-of-distribution conditions. Our analysis highlights the critical role of the graph homophily assumption in shaping the effectiveness of uncertainty estimates. Extensive experiments demonstrate that CGNNs deliver more reliable representations of epistemic uncertainty and achieve state-of-the-art performance under distributional shift on heterophilic graphs.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 39 of its 50 references that are in the map. First posted on arXiv 2 Dec 2025.

Builds on

39 of 50 references.

References · 39

  1. 2017Inductive Representation Learning on Large Graphscited by 6,320
  2. 2017On Calibration of Modern Neural Networkscited by 4,960
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  5. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  6. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  7. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  8. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  9. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  10. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,287
  11. 2018Pitfalls of Graph Neural Network Evaluationcited by 1,007
  12. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
27 more
  1. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  2. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 600
  3. 2019Multi-scale Attributed Node Embeddingcited by 548
  4. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 311
  5. 2019Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Visioncited by 182
  6. 2021Is Homophily a Necessity for Graph Neural Networks?cited by 182
  7. 2019Distributionally Robust Language Modelingcited by 143
  8. 2019A Gentle Introduction to Deep Learning for Graphscited by 108
  9. 2020Uncertainty Aware Semi-Supervised Learning on Graph Datacited by 95
  10. 2022A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglementcited by 87
  11. 2020Bayesian Graph Neural Networks with Adaptive Connection Samplingcited by 81
  12. 2021Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classificationcited by 80
  13. 2023Energy-based Out-of-Distribution Detection for Graph Neural Networkscited by 68
  14. 2022Deep Ensembles Work, But Are They Necessary?cited by 63
  15. 2024Credal Learning Theorycited by 31
  16. 2024The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challengescited by 29
  17. 2024CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Taskscited by 26
  18. 2024Uncertainty in Graph Neural Networks: A Surveycited by 23
  19. 2022The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalizationcited by 21
  20. 2024Energy-based Epistemic Uncertainty for Graph Neural Networkscited by 14
  21. 2022A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networkscited by 12
  22. 2020Efficient Computation Reduction in Bayesian Neural Networks Through Feature Decomposition and Memorizationcited by 10
  23. 2022Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecastingcited by 10
  24. 2023Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forcescited by 10
  25. 2023Addressing Heterophily in Node Classification with Graph Echo State Networkscited by 4
  26. 2024Uncertainty Quantification on Graph Learning: A Surveycited by 4
  27. 2025Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theorycited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Random-Set Graph Neural Networkscited by 3
  2. 2026A formal definition and meta-model for a machine theory of mindcited by 1