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.

翻译：图机器学习在现实领域有诸多应用，如节点/图分类和链接预测。为图机器学习模型提供人类可理解的解释是一项具有挑战性但基础性的任务，有助于促进其应用推广，然而针对链接预测模型解释的验证工作却鲜有关注。本文提出了用于评估链接预测解释质量的量化指标，该指标可在有或无真实标签的情况下使用。我们利用这些指标评估了当前最先进的图神经网络可解释性方法，并讨论了链接预测任务中特有的潜在假设和技术细节（如节点嵌入间距离的选择）如何影响解释质量。