mlsanityData to 14 Oct 2026

Uncertainty-Aware Robust Learning on Noisy Graphs

arXiv 2306.08210 ↗First posted 14 Jun 2023cs.LGcited by 6

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Abstract (arXiv)

Graph neural networks (GNNs) have excelled in various graph learning tasks, particularly node classification. However, their performance is often hampered by noisy measurements in real-world graphs, which can corrupt critical patterns in the data. To address this, we propose a novel uncertainty-aware graph learning framework inspired by distributionally robust optimization. Specifically, we use a graph neural network-based encoder to embed the node features and find the optimal node embeddings by minimizing the worst-case risk through a minimax formulation. Such an uncertainty-aware learning process leads to improved node representations and a more robust graph predictive model that effectively mitigates the impact of uncertainty arising from data noise. Our experimental results demonstrate superior predictive performance over baselines across noisy scenarios.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 31 of its 45 references that are in the map. First posted on arXiv 14 Jun 2023.

Builds on

31 of 45 references.

References · 31

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  4. 2018Deep Graph Infomaxcited by 1,106
  5. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,102
  6. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
  7. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 976
  8. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  9. 2017OptNet: Differentiable Optimization as a Layer in Neural Networkscited by 599
  10. 2019Differentiable Convex Optimization Layerscited by 465
  11. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 371
  12. 2018Learning Models with Uniform Performance via Distributionally Robust Optimizationcited by 335
19 more
  1. 2020Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddingscited by 214
  2. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 133
  3. 2020Learning to Drop: Robust Graph Neural Network via Topological Denoisingcited by 127
  4. 2015Distributionally Robust Logistic Regressioncited by 126
  5. 2021A Survey on Graph Structure Learning: Progress and Opportunitiescited by 71
  6. 2021On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Featurescited by 63
  7. 2016Minimizing the Maximal Loss: How and Why?cited by 30
  8. 2018Robust Hypothesis Testing Using Wasserstein Uncertainty Setscited by 28
  9. 2019Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learningcited by 21
  10. 2021DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samplescited by 18
  11. 2020Incomplete Graph Representation and Learning via Partial Graph Neural Networkscited by 16
  12. 2023Few-shot Node Classification with Extremely Weak Supervisioncited by 14
  13. 2022Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcitycited by 11
  14. 2019Learning Robust Representations with Graph Denoising Policy Networkcited by 10
  15. 2022Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attackcited by 10
  16. 2020Distributionally Robust Weighted k-Nearest Neighborscited by 9
  17. 2021Distributionally Robust Semi-Supervised Learning Over Graphscited by 9
  18. 2018Distributionally Robust Graphical Modelscited by 3
  19. 2021Robust Graph Learning Under Wasserstein Uncertaintycited by 3

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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 3.

6 papers.

  1. 2025Gen-DFL: Decision-Focused Generative Learning for Robust Decision Makingcited by 18
  2. 2025"X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systemscited by 7
  3. 2024Uncertainty Quantification on Graph Learning: A Surveycited by 4
  4. 2025Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Managementcited by 4
  5. 2024Enhancing the Resilience of Graph Neural Networks to Topological Perturbations in Sparse Graphscited by 3
  6. 2023Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributescited by 1