Graph neural networks (GNNs) are often miscalibrated for link prediction tasks, exhibiting a mixed behavior of overconfidence in negative predictions and underconfidence in positive ones. The proposed method, IN-N-OUT, effectively calibrates GNNs for link prediction tasks.
GNNs for link prediction exhibit miscalibration, requiring calibration methods like IN-N-OUT to improve accuracy.