mlsanityData to 7 Oct 2026

Conformal Inductive Graph Neural Networks

arXiv 2407.09173 ↗First posted 12 Jul 2024cs.LGcited by 9

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Abstract (arXiv)

Conformal prediction (CP) transforms any model's output into prediction sets guaranteed to include (cover) the true label. CP requires exchangeability, a relaxation of the i.i.d. assumption, to obtain a valid distribution-free coverage guarantee. This makes it directly applicable to transductive node-classification. However, conventional CP cannot be applied in inductive settings due to the implicit shift in the (calibration) scores caused by message passing with the new nodes. We fix this issue for both cases of node and edge-exchangeable graphs, recovering the standard coverage guarantee without sacrificing statistical efficiency. We further prove that the guarantee holds independently of the prediction time, e.g. upon arrival of a new node/edge or at any subsequent moment.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 12 Jul 2024.

Builds on

20 of 34 references.

References · 20

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  4. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  5. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  6. 2015Image-based Recommendations on Styles and Substitutescited by 702
  7. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  8. 2019Conformal Prediction Under Covariate Shiftcited by 511
  9. 2020Classification with Valid and Adaptive Coveragecited by 390
  10. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 365
  11. 2022Conformal prediction beyond exchangeabilitycited by 348
  12. 2016Least Ambiguous Set-Valued Classifiers with Bounded Error Levelscited by 270
8 more
  1. 2019Predictive inference with the jackknife+cited by 206
  2. 2021Learning Optimal Conformal Classifierscited by 108
  3. 2021Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classificationcited by 79
  4. 2023Uncertainty Quantification over Graph with Conformalized Graph Neural Networkscited by 79
  5. 2022Conformal Prediction Sets with Limited False Positivescited by 28
  6. 2022Distribution Free Prediction Sets for Node Classificationcited by 28
  7. 2013Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structurescited by 22
  8. 2016Edge-exchangeable graphs and sparsitycited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 2.

9 papers.

  1. 2024Conformal Prediction: A Data Perspectivecited by 25
  2. 2025Robust Conformal Prediction with a Single Binary Certificatecited by 4
  3. 2024Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphscited by 1
  4. 2024RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classificationcited by 0
  5. 2024Focus Where It Matters: Graph Selective State Focused Attention Networkscited by 0
  6. 2025EvA: Evolutionary Attacks on Graphscited by 0
  7. 2025Conformal Prediction for Multi-Source Detection on a Networkcited by 0
  8. 2026Questioning the Coverage-Length Metric in Conformal Prediction: When Shorter Intervals Are Not Bettercited by 0
  9. 2026GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphscited by 0