Graph Neural Networks (GNNs) have proven to be successful in many classification tasks, outperforming previous state-of-the-art methods in terms of accuracy. However, accuracy alone is not enough for high-stakes decision making. Decision makers want to know the likelihood that a specific GNN prediction is correct. For this purpose, obtaining calibrated models is essential. In this work, we perform an empirical evaluation of the calibration of state-of-the-art GNNs on multiple datasets. Our experiments show that GNNs can be calibrated in some datasets but also badly miscalibrated in others, and that state-of-the-art calibration methods are helpful but do not fix the problem.
@article{arxiv.1905.02296,
title = {Are Graph Neural Networks Miscalibrated?},
author = {Leonardo Teixeira and Brian Jalaian and Bruno Ribeiro},
journal= {arXiv preprint arXiv:1905.02296},
year = {2019}
}
Comments
Presented at the ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data