  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. In this paper, we provide
quantitative metrics to assess the quality of link prediction explanations,
with or without ground-truth. State-of-the-art explainability methods for Graph
Neural Networks are evaluated using these metrics. We discuss how underlying
assumptions and technical details specific to the link prediction task, such as
the choice of distance between node embeddings, can influence the quality of
the explanations.
