Given the importance of getting calibrated predictions and reliable uncertainty estimations, various post-hoc calibration methods have been developed for neural networks on standard multi-class classification tasks. However, these methods are not well suited for calibrating graph neural networks (GNNs), which presents unique challenges such as the additional structural information and the graph-induced correlations between the nodes. In this work, we conduct a systematic study on the calibration…