Abstract
We present a new, simple and effective loss function for calibrating graph neural networks (GNNs). Miscalibration is the problem whereby a model's probabilities does not reflect it's correctness, making it difficult and possibly dangerous for real-world deployment. We compare our method against other baselines on a novel ID and OOD graph form of the Celeb-A faces dataset. Our findings show that our method improves calibration for GNNs, which are not immune to miscalibration in-distribution (ID) and out-of-distribution (OOD). Our code is available for review at https://github.com/dexterdley/CS6208/tree/main/Project.
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