mlsanityData to 7 Oct 2026

Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training

arXiv 2501.02767 ↗First posted 6 Jan 2025cs.LG cs.AIcited by 6

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Abstract (arXiv)

Graph Neural Networks (GNNs) has been widely used in a variety of fields because of their great potential in representing graph-structured data. However, lacking of rigorous uncertainty estimations limits their application in high-stakes. Conformal Prediction (CP) can produce statistically guaranteed uncertainty estimates by using the classifier's probability estimates to obtain prediction sets, which contains the true class with a user-specified probability. In this paper, we propose a Rank-based CP during training framework to GNNs (RCP-GNN) for reliable uncertainty estimates to enhance the trustworthiness of GNNs in the node classification scenario. By exploiting rank information of the classifier's outcome, prediction sets with desired coverage rate can be efficiently constructed. The strategy of CP during training with differentiable rank-based conformity loss function is further explored to adapt prediction sets according to network topology information. In this way, the composition of prediction sets can be guided by the goal of jointly reducing inefficiency and probability estimation errors. Extensive experiments on several real-world datasets show that our model achieves any pre-defined target marginal coverage while significantly reducing the inefficiency compared with state-of-the-art methods.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 25 of its 33 references that are in the map. First posted on arXiv 6 Jan 2025.

Builds on

25 of 33 references.

References · 25

  1. 2017On Calibration of Modern Neural Networkscited by 4,860
  2. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  3. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  4. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 493
  5. 2020Classification with Valid and Adaptive Coveragecited by 390
  6. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 365
  7. 2019Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibrationcited by 359
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  10. 2018To Trust Or Not To Trust A Classifiercited by 205
  11. 2020Fast Differentiable Sorting and Rankingcited by 196
  12. 2020Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learningcited by 190
13 more
  1. 2020Calibration of Neural Networks using Splinescited by 97
  2. 2021Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibrationcited by 91
  3. 2013A Conformal Prediction Approach to Explore Functional Datacited by 85
  4. 2021Graph Representation Learning in Biomedicinecited by 79
  5. 2021Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classificationcited by 79
  6. 2022What Makes Graph Neural Networks Miscalibrated?cited by 39
  7. 2022Distribution Free Prediction Sets for Node Classificationcited by 28
  8. 2021Optimized conformal classification using gradient descent approximationcited by 27
  9. 2024Trustworthy Classification through Rank-Based Conformal Prediction Setscited by 20
  10. 2023Conformal Prediction for Network-Assisted Regressioncited by 14
  11. 2020On Calibration of Scene-Text Recognition Modelscited by 9
  12. 2023Conformal link prediction for false discovery rate controlcited by 9
  13. 2020Wisdom of the Ensemble: Improving Consistency of Deep Learning Modelscited by 7

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Cited by year

Citing papers per year: 2025: 6.

6 papers.

  1. 2025Volume-Sorted Prediction Set: Efficient Conformal Prediction for Multi-Target Regressioncited by 6
  2. 2025Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliabilitycited by 6
  3. 2025Enhanced Route Planning with Calibrated Uncertainty Setcited by 5
  4. 2025Conditional Conformal Risk Adaptationcited by 5
  5. 2025Residual Reweighted Conformal Prediction for Graph Neural Networkscited by 2
  6. 2025CoFineLLM: Conformal Finetuning of LLMs for Language-Instructed Robot Planningcited by 0