Evaluating Link Prediction Explanations for Graph Neural Networks

Claudio Borile, Alan Perotti, André Panisson

Abstract: Graph Machine Learning (GML) has numerous applications, such as node/graph classification and link prediction, in real-world domains. Providing human-understandable explanations for GML models is a challenging yet fundamental task to foster their adoption, but validating explanations for link prediction models has received little attention.