While instance-level explanation of GNN is a well-studied problem with plenty of approaches being developed, providing a global explanation for the behaviour of a GNN is much less explored, despite its potential in interpretability and debugging. Existing solutions either simply list local explanations for a given class, or generate a synthetic prototypical graph with maximal score for a given class, completely missing any combinatorial aspect that the GNN could have learned. In this work, we propose GLGExplainer (Global Logic-based GNN Explainer), the first Global Explainer capable of generating explanations as arbitrary Boolean combinations of learned graphical concepts. GLGExplainer is a fully differentiable architecture that takes local explanations as inputs and combines them into a logic formula over graphical concepts, represented as clusters of local explanations. Contrary to existing solutions, GLGExplainer provides accurate and human-interpretable global explanations that are perfectly aligned with ground-truth explanations (on synthetic data) or match existing domain knowledge (on real-world data). Extracted formulas are faithful to the model predictions, to the point of providing insights into some occasionally incorrect rules learned by the model, making GLGExplainer a promising diagnostic tool for learned GNNs.

翻译：尽管图神经网络（GNN）的实例级解释已成为一个研究充分的问题，并已开发出大量方法，但针对GNN行为提供全局解释的研究仍显不足，尽管其在可解释性与调试方面具有巨大潜力。现有解决方案要么简单列出给定类别的局部解释，要么生成具有该类最大得分的合成原型图，完全忽略了GNN可能学习的任何组合性特征。本文提出GLGExplainer（基于全局逻辑的GNN解释器）——首个能够将解释生成为所学图形概念的任意布尔组合的全局解释器。GLGExplainer是一个完全可微的架构，它接收局部解释作为输入，并将其组合成图形概念上的逻辑公式，这些概念以局部解释的聚类表示。与现有解决方案不同，GLGExplainer能够提供准确且人类可理解的全局解释，这些解释与真实解释（在合成数据上）完美对齐，或符合现有领域知识（在真实世界数据上）。提取的公式忠实于模型预测，甚至能揭示模型偶尔学习的错误规则，这使得GLGExplainer成为GNN学习过程中一种有前景的诊断工具。