Graph Neural Networks (GNNs) are widely used for node classification, yet their opaque decision-making limits trust and adoption. While local explanations offer insights into individual predictions, global explanation methods, those that characterize an entire class, remain underdeveloped. Existing global explainers rely on motif discovery in small graphs, an approach that breaks down in large, real-world settings where subgraph repetition is rare, node attributes are high-dimensional, and predictions arise from complex structure-attribute interactions. We propose GnnXemplar, a novel global explainer inspired from Exemplar Theory from cognitive science. GnnXemplar identifies representative nodes in the GNN embedding space, exemplars, and explains predictions using natural language rules derived from their neighborhoods. Exemplar selection is framed as a coverage maximization problem over reverse k-nearest neighbors, for which we provide an efficient greedy approximation. To derive interpretable rules, we employ a self-refining prompt strategy using large language models (LLMs). Experiments across diverse benchmarks show that GnnXemplar significantly outperforms existing methods in fidelity, scalability, and human interpretability, as validated by a user study with 60 participants.

翻译：图神经网络（GNNs）被广泛应用于节点分类任务，但其不透明的决策过程限制了可信度与采用范围。尽管局部解释方法能为个体预测提供洞见，但全局解释方法——即刻画整个类别特征的方法——仍发展不足。现有全局解释器依赖于在小图中进行模体发现，这一方法在大型实际场景中失效，因为此类场景中子图重复罕见、节点属性高维且预测源于复杂的结构-属性交互。我们提出GnnXemplar，一种受认知科学中范例理论启发的新型全局解释器。GnnXemplar在GNN嵌入空间中识别代表性节点（即范例），并通过从其邻域推导的自然语言规则解释预测。范例选择被构建为反向k近邻上的覆盖最大化问题，我们为此提供了一种高效的贪心近似算法。为生成可解释规则，我们采用基于大语言模型（LLMs）的自优化提示策略。在多种基准测试上的实验表明，GnnXemplar在保真度、可扩展性和人类可解释性方面显著优于现有方法，这通过一项包含60名参与者的用户研究得到了验证。