The success of Graph Neural Networks (GNNs) has led to a need for understanding their decision-making process and providing explanations for their predictions, which has given rise to explainable AI (XAI) that offers transparent explanations for black-box models. Recently, the use of prototypes has successfully improved the explainability of models by learning prototypes to imply training graphs that affect the prediction. However, these approaches tend to provide prototypes with excessive information from the entire graph, leading to the exclusion of key substructures or the inclusion of irrelevant substructures, which can limit both the interpretability and the performance of the model in downstream tasks. In this work, we propose a novel framework of explainable GNNs, called interpretable Prototype-based Graph Information Bottleneck (PGIB) that incorporates prototype learning within the information bottleneck framework to provide prototypes with the key subgraph from the input graph that is important for the model prediction. This is the first work that incorporates prototype learning into the process of identifying the key subgraphs that have a critical impact on the prediction performance. Extensive experiments, including qualitative analysis, demonstrate that PGIB outperforms state-of-the-art methods in terms of both prediction performance and explainability.

翻译：图神经网络（GNNs）的成功导致需要理解其决策过程并为其预测提供解释，这催生了可解释人工智能（XAI），它为黑盒模型提供了透明的解释。近年来，原型（proto-types）的使用通过学习原型来隐含影响预测的训练图，从而成功提升了模型的可解释性。然而，这些方法倾向于从整个图中提供包含过多信息的原型，这可能导致关键子结构被排除或无关子结构被包含，从而限制了下游任务中模型的可解释性和性能。在这项工作中，我们提出了一种新的可解释GNN框架，称为可解释的原型图信息瓶颈（PGIB），它在信息瓶颈框架内融入原型学习，以从输入图中提供对于模型预测重要的关键子图的原型。这是首个将原型学习融入识别对预测性能有关键影响的关键子图过程的工作。包括定性分析在内的大量实验表明，PGIB在预测性能和可解释性方面均优于现有最先进方法。