Beyond improving trust and validating model fairness, xAI practices also have the potential to recover valuable scientific insights in application domains where little to no prior human intuition exists. To that end, we propose a method to extract global concept explanations from the predictions of graph neural networks to develop a deeper understanding of the tasks underlying structure-property relationships. We identify concept explanations as dense clusters in the self-explaining Megan models subgraph latent space. For each concept, we optimize a representative prototype graph and optionally use GPT-4 to provide hypotheses about why each structure has a certain effect on the prediction. We conduct computational experiments on synthetic and real-world graph property prediction tasks. For the synthetic tasks we find that our method correctly reproduces the structural rules by which they were created. For real-world molecular property regression and classification tasks, we find that our method rediscovers established rules of thumb. More specifically, our results for molecular mutagenicity prediction indicate more fine-grained resolution of structural details than existing explainability methods, consistent with previous results from chemistry literature. Overall, our results show promising capability to extract the underlying structure-property relationships for complex graph property prediction tasks.

翻译：除了提升可信度和验证模型公平性外，可解释人工智能（xAI）实践还有潜力在缺乏人类先验直觉的应用领域中提取有价值的科学见解。为此，我们提出了一种方法，从图神经网络的预测中提取全局概念解释，以深入理解任务中蕴含的结构-性质关系。我们将概念解释定义为自解释Megan模型子图潜在空间中的密集聚类。针对每个概念，我们优化一个代表性原型图，并可选择使用GPT-4提供关于每个结构为何对预测产生特定影响的假设。我们在合成和真实世界的图属性预测任务上进行了计算实验。对于合成任务，我们发现该方法能准确复现其生成过程中所依据的结构规则。对于真实世界的分子属性回归与分类任务，我们的方法重新发现了已有经验规则。具体而言，在分子致突变性预测任务中，我们的结果揭示了比现有可解释性方法更精细的结构细节分辨率，这与化学文献中的既有结论一致。总体而言，我们的结果表明，该方法在提取复杂图属性预测任务中潜在的结构-性质关系方面具有令人期待的能力。