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RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification

S. Akansha

TL;DR

RoCP-GNN addresses robust uncertainty estimation for node classification in graphs by embedding split-conformal prediction into end-to-end GNN training. It introduces a size-loss and differentiable quantile mechanism to produce compact, valid prediction sets, achieving marginal coverage of $1-\epsilon$ under exchangeability while preserving or improving accuracy across multiple GNN backbones on standard benchmarks. Empirically, RoCP-GNN yields substantial reductions in prediction-set size (inefficiency) with consistent coverage and competitive accuracy on Cora, Citeseer, and PubMed. This work shows that conformal prediction can be efficiently integrated into graph SSL pipelines, enabling reliable and scalable uncertainty quantification for high-stakes graph tasks.

Abstract

Graph Neural Networks (GNNs) have emerged as powerful tools for predicting outcomes in graph-structured data. However, a notable limitation of GNNs is their inability to provide robust uncertainty estimates, which undermines their reliability in contexts where errors are costly. One way to address this issue is by providing prediction sets that contain the true label with a predefined probability margin. Our approach builds upon conformal prediction (CP), a framework that promises to construct statistically robust prediction sets or intervals. There are two primary challenges: first, given dependent data like graphs, it is unclear whether the critical assumption in CP - exchangeability - still holds when applied to node classification. Second, even if the exchangeability assumption is valid for conformalized link prediction, we need to ensure high efficiency, i.e., the resulting prediction set or the interval length is small enough to provide useful information. In this article, we propose a novel approach termed Robust Conformal Prediction for GNNs (RoCP-GNN), which integrates conformal prediction (CP) directly into the GNN training process. This method generates prediction sets, instead of just point predictions, that are valid at a user-defined confidence level, assuming only exchangeability. Our approach robustly predicts outcomes with any predictive GNN model while quantifying the uncertainty in predictions within the realm of graph-based semi-supervised learning (SSL). Experimental results demonstrate that GNN models with size loss provide a statistically significant increase in performance. We validate our approach on standard graph benchmark datasets by coupling it with various state-of-the-art GNNs in node classification. The code will be made available after publication.

RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification

TL;DR

RoCP-GNN addresses robust uncertainty estimation for node classification in graphs by embedding split-conformal prediction into end-to-end GNN training. It introduces a size-loss and differentiable quantile mechanism to produce compact, valid prediction sets, achieving marginal coverage of under exchangeability while preserving or improving accuracy across multiple GNN backbones on standard benchmarks. Empirically, RoCP-GNN yields substantial reductions in prediction-set size (inefficiency) with consistent coverage and competitive accuracy on Cora, Citeseer, and PubMed. This work shows that conformal prediction can be efficiently integrated into graph SSL pipelines, enabling reliable and scalable uncertainty quantification for high-stakes graph tasks.

Abstract

Graph Neural Networks (GNNs) have emerged as powerful tools for predicting outcomes in graph-structured data. However, a notable limitation of GNNs is their inability to provide robust uncertainty estimates, which undermines their reliability in contexts where errors are costly. One way to address this issue is by providing prediction sets that contain the true label with a predefined probability margin. Our approach builds upon conformal prediction (CP), a framework that promises to construct statistically robust prediction sets or intervals. There are two primary challenges: first, given dependent data like graphs, it is unclear whether the critical assumption in CP - exchangeability - still holds when applied to node classification. Second, even if the exchangeability assumption is valid for conformalized link prediction, we need to ensure high efficiency, i.e., the resulting prediction set or the interval length is small enough to provide useful information. In this article, we propose a novel approach termed Robust Conformal Prediction for GNNs (RoCP-GNN), which integrates conformal prediction (CP) directly into the GNN training process. This method generates prediction sets, instead of just point predictions, that are valid at a user-defined confidence level, assuming only exchangeability. Our approach robustly predicts outcomes with any predictive GNN model while quantifying the uncertainty in predictions within the realm of graph-based semi-supervised learning (SSL). Experimental results demonstrate that GNN models with size loss provide a statistically significant increase in performance. We validate our approach on standard graph benchmark datasets by coupling it with various state-of-the-art GNNs in node classification. The code will be made available after publication.
Paper Structure (17 sections, 8 equations, 3 figures, 4 tables)

This paper contains 17 sections, 8 equations, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background and problem framework
  3. Problem settings
  4. Graph Neural Networks in Node Classification
  5. Conformal Prediction
  6. Adaptive Prediction Set (APS)
  7. Evaluation metrics
  8. Conformal Prediction in Graph Neural Networks
  9. RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks
  10. RoCP Implementation
  11. Experiments and Results
  12. Datasets, Models, and Evaluation
  13. Inefficiency Results
  14. RoCP Maintains Accuracy
  15. Coverage
  16. ...and 2 more sections

Figures (3)

  • Figure 1: RoCP training illustration: This illustration demonstrates the key components of Robust conformal Prediction (RoCP) training.
  • Figure 2: These bar plots depict the effect of the calibration dataset size, $D_{train-calib}$, on the accuracy of classifier models trained under RoCP, corresponding to calib_frac values of $\{0.3, 0.4, 0.5, 0.6\}$. The results are compared to standard cross-entropy training, represented by calib_frac = 0.0, across various GNN models.
  • Figure 3: These bar plots illustrate the cumulative impact of the calibration dataset size, $D_{train-calib}$, on the prediction set size for different models trained with RoCP, corresponding to calib_frac values of $\{0.3, 0.4, 0.5, 0.6\}$. The results are compared to models using only CP, represented by calib_frac = 0.0, across various GNN models.