Calibration for Link Prediction

Calibration for Link Prediction refers to the process of adjusting the output probabilities of a prediction model in graph-structured data to more accurately reflect the actual likelihood of link existence. This method aims to enhance the reliability of model predictions, ensuring that the probability distribution of the prediction results is consistent with the real situation. As such, it plays a crucial role in fields such as recommendation systems, social network analysis, and bioinformatics, improving the accuracy of decision support.