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透明性与可解释性已成为重要议题。对模型行为进行合理解释不仅能增强模型透明度，还能帮助研究者开发更负责任的模型。图神经网络在众多图机器学习问题中已展现出优于传统方法的性能，其可解释性研究也日益受到关注。然而，现有文献中对链接预测任务的GNN解释研究尚存空白。链接预测作为GNN的核心任务，对应着推荐系统、网络搜索等实际应用场景。针对现有GNN解释方法仅支持节点/图级任务的问题，我们提出面向异构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，并能处理图的异构性。定性层面，PaGE-Link可生成连接节点对的路径形式解释，自然捕捉节点间关联并易于转化为人类可理解的解释；定量层面，该方法在引文网络与用户-物品图上的推荐任务中，AUC指标提升达9%-35%，且在人机评估中78.79%的响应者选择了该解释为更优方案。