Conformal load prediction with transductive graph autoencoders

Predicting edge weights on graphs has various applications, from transportation systems to social networks. This paper describes a Graph Neural Network (GNN) approach for edge weight prediction with guaranteed coverage. We leverage conformal prediction to calibrate the GNN outputs and produce valid...

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Bibliographic Details
Published in:Machine learning 2025-03, Vol.114 (3), p.54, Article 54
Main Authors: Luo, Rui, Colombo, Nicolo
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Control
Graph neural networks
Machine Learning
Mechatronics
Natural Language Processing (NLP)
Predictions
Robotics
Simulation and Modeling
Social networks
Transportation systems
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Description
Summary:Predicting edge weights on graphs has various applications, from transportation systems to social networks. This paper describes a Graph Neural Network (GNN) approach for edge weight prediction with guaranteed coverage. We leverage conformal prediction to calibrate the GNN outputs and produce valid prediction intervals. We handle data heteroscedasticity through error reweighting and Conformalized Quantile Regression (CQR). We compare the performance of our method against baseline techniques on real-world transportation datasets. Our approach has better coverage and efficiency than all baselines and showcases robustness and adaptability.
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-024-06713-w