mlsanityData to 14 Oct 2026

Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective

arXiv 2206.10691 ↗First posted 21 Jun 2022cs.LGcited by 13

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Abstract (arXiv)

The problem of out-of-distribution detection for graph classification is far from being solved. The existing models tend to be overconfident about OOD examples or completely ignore the detection task. In this work, we consider this problem from the uncertainty estimation perspective and perform the comparison of several recently proposed methods. In our experiment, we find that there is no universal approach for OOD detection, and it is important to consider both graph representations and predictive categorical distribution.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 21 Jun 2022.

Builds on

12 of 20 references.

References · 12

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  3. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 732
  4. 2018Predictive Uncertainty Estimation via Prior Networkscited by 557
  5. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  6. 2020Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Countscited by 164
  7. 2022Handling Distribution Shifts on Graphs: An Invariance Perspectivecited by 164
  8. 2022Out-Of-Distribution Generalization on Graphs: A Surveycited by 97
  9. 2020Uncertainty Aware Semi-Supervised Learning on Graph Datacited by 95
  10. 2021Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classificationcited by 80
  11. 2020Uncertainty Estimation Using a Single Deep Deterministic Neural Networkcited by 37
  12. 2022Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Spacecited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 5; 2026: 5.

13 papers.

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  1. 2023Energy-based Out-of-Distribution Detection for Graph Neural Networkscited by 68
  2. 2024Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advancescited by 26
  3. 2024Uncertainty in Graph Neural Networks: A Surveycited by 23
  4. 2025GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generationcited by 15
  5. 2025Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphscited by 9
  6. 2025Bounded and Uniform Energy-based Out-of-distribution Detection for Graphscited by 6
  7. 2026Random-Set Graph Neural Networkscited by 3
  8. 2025Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theorycited by 2
  9. 2026Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?cited by 2
  10. 2025Harnessing Feature Resonance under Arbitrary Target Alignment for Out-of-Distribution Node Detectioncited by 1
  11. 2026LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphscited by 1
  12. 2026What Information Matters? Graph Out-of-Distribution Detection via Tri-Component Information Decompositioncited by 1
1 more
  1. 2026Quantile-Free Uncertainty Quantification in Graph Neural Networkscited by 0