通过对比学习实现图的全局概念解释
Global Concept Explanations for Graphs by Contrastive Learning
AI总结：
提出从图神经网络预测中提取全局概念解释的方法，通过识别自解释Megan模型潜在空间中的密集簇并优化原型图，结合GPT-4生成假设，在合成与真实分子任务中验证了其恢复结构规则和经验法则的能力。
摘要：
除了提高信任度和验证模型公平性外，xAI实践还有潜力在几乎不存在人类先验直觉的应用领域中恢复有价值的科学见解。为此，我们提出了一种从图神经网络预测中提取全局概念解释的方法，以深入理解任务背后的结构-性质关系。我们将概念解释识别为自解释Megan模型子图潜在空间中的密集簇。对于每个概念，我们优化一个代表性原型图，并选择性地使用GPT-4提供关于为何每种结构对预测有特定影响的假设。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，我们发现该方法正确重现了其创建时的结构规则。对于真实世界的分子属性回归和分类任务，我们发现该方法重新发现了既定的经验法则。更具体地说，我们在分子致突变性预测方面的结果表明，该方法对结构细节的解析度比现有可解释性方法更精细，这与化学文献的先前结果一致。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背后的结构-性质关系方面具有良好潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
