Transparency and accountability have become major concerns for black-box machine learning (ML) models. Proper explanations for the model behavior increase model transparency and help researchers develop more accountable models. Graph neural networks (GNN) have recently shown superior performance in many graph ML problems than traditional methods, and explaining them has attracted increased interest. However, GNN explanation for link prediction (LP) is lacking in the literature. LP is an essential GNN task and corresponds to web applications like recommendation and sponsored search on web. Given existing GNN explanation methods only address node/graph-level tasks, we propose Path-based GNN Explanation for heterogeneous Link prediction (PaGE-Link) that generates explanations with connection interpretability, enjoys model scalability, and handles graph heterogeneity. Qualitatively, PaGE-Link can generate explanations as paths connecting a node pair, which naturally captures connections between the two nodes and easily transfer to human-interpretable explanations. Quantitatively, explanations generated by PaGE-Link improve AUC for recommendation on citation and user-item graphs by 9 - 35% and are chosen as better by 78.79% of responses in human evaluation.

翻译：黑盒机器学习模型的可解释性与问责性已成为主要关注点。对模型行为进行恰当解释可提升模型透明度，并帮助研究人员开发更负责任的模型。图神经网络（GNN）近期在诸多图机器学习问题上展现出优于传统方法的性能，其可解释性研究也因此受到广泛关注。然而，现有文献中关于链接预测（LP）任务的GNN解释方法仍存在空白。链接预测作为GNN的核心任务，对应推荐系统和网络搜索中的赞助搜索等网络应用场景。针对现有GNN解释方法仅能处理节点级或图级任务的问题，我们提出面向异构图链接预测的基于路径的GNN解释方法（PaGE-Link），该方法具备连接可解释性、模型可扩展性，并能够处理图异构性。在定性分析层面，PaGE-Link可生成连接节点对的路径作为解释，这些路径天然刻画了节点间的关联关系，并易于转化为人类可理解的形式。在定量评估层面，PaGE-Link生成的解释使引文网络和用户-物品图的推荐AUC值提升9%-35%，且在人工评估中，78.79%的受试者认为其解释效果更优。