Are Graph Neural Networks Miscalibrated?

Leonardo Teixeira; Brian Jalaian; Bruno Ribeiro

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arXiv · 1905.02296

Are Graph Neural Networks Miscalibrated?

Leonardo Teixeira·Brian Jalaian·Bruno Ribeiro

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Abstract

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.

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Leonardo Teixeira, Brian Jalaian, Bruno Ribeiro. 2019-05-07. Are Graph Neural Networks Miscalibrated?. https://arxiv.org/abs/1905.02296

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