Researchers have developed a new framework for node classification in Graph Neural Networks (GNNs). The approach uses minimal abductive explanations as an intermediate representation to extract global logical rules. This method is designed to improve the generality of extracted rules by reducing redundant structural information. Experiments on benchmark datasets show that the proposed framework produces compact global rules while maintaining high fidelity to the original model. --- Why it matters: This work matters to researchers in AI because it addresses a limitation of existing logic-based approaches for GNNs, which can be limited by redundant structural information. The proposed framework has the potential to improve the interpretability and explainability of GNNs, making them more reliable and trustworthy in real-world applications. Source: https://arxiv.org/abs/2608.17103