  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.
