Aside from graph neural networks (GNNs) attracting significant attention as a powerful framework revolutionizing graph representation learning, there has been an increasing demand for explaining GNN models. Although various explanation methods for GNNs have been developed, most studies have focused on instance-level explanations, which produce explanations tailored to a given graph instance. In our study, we propose Prototype-bAsed GNN-Explainer (PAGE), a novel model-level GNN explanation method that explains what the underlying GNN model has learned for graph classification by discovering human-interpretable prototype graphs. Our method produces explanations for a given class, thus being capable of offering more concise and comprehensive explanations than those of instance-level explanations. First, PAGE selects embeddings of class-discriminative input graphs on the graph-level embedding space after clustering them. Then, PAGE discovers a common subgraph pattern by iteratively searching for high matching node tuples using node-level embeddings via a prototype scoring function, thereby yielding a prototype graph as our explanation. Using six graph classification datasets, we demonstrate that PAGE qualitatively and quantitatively outperforms the state-of-the-art model-level explanation method. We also carry out systematic experimental studies by demonstrating the relationship between PAGE and instance-level explanation methods, the robustness of PAGE to input data scarce environments, and the computational efficiency of the proposed prototype scoring function in PAGE.

翻译：尽管图神经网络（GNNs）作为革新图表示学习的强大框架备受关注，对其模型进行解释的需求也日益增长。现有GNN解释方法虽已取得诸多进展，但大多数研究聚焦于实例级解释——即为特定图实例生成定制化解释。本研究提出原型驱动的GNN解释器（PAGE），这是一种新颖的模型级GNN解释方法，通过发现人类可理解的原型图，揭示底层GNN模型在图分类任务中学到的知识。该方法针对特定类别生成解释，因此能够提供比实例级解释更简洁且全面的解释。首先，PAGE在聚类后选取图级嵌入空间中具有类别判别性的输入图嵌入；然后通过原型评分函数，利用节点级嵌入迭代搜索高匹配节点元组，发现共同子图模式，最终生成原型图作为解释。基于六个图分类数据集的实验表明，PAGE在定性与定量评估上均显著超越现有最先进的模型级解释方法。我们还通过系统实验研究，揭示了PAGE与实例级解释方法的关系、其在数据稀缺环境下的鲁棒性以及所提原型评分函数的计算效率。