AI 中文总结
本研究针对SGC节点分类任务，提出以最小溯因解释为中间表示的逻辑框架，可提取紧凑且高保真的全局逻辑规则。
AI 中文摘要
图神经网络（GNN）在节点分类任务中取得了显著性能，促使人们对能够解释其预测结果的方法产生越来越浓厚的兴趣。近期基于逻辑的方法（如LogicXGNN）从解释性子图集合中推导图神经网络（GNN）的全局逻辑规则。这些子图虽具信息性，但可能包含特定于单个节点的冗余结构信息，潜在限制了提取规则的通用性。本研究针对简单图卷积（SGC）网络的节点分类，提出一种基于逻辑的框架，该框架以最小溯因解释作为规则提取的中间表示。对每个节点，计算足以保留其预测类别的最小节点-特征对集合；随后利用这些解释训练决策树，进而从中提取全局逻辑规则。在基准数据集上的实验表明，该框架生成的全局规则紧凑，同时保持了与原始SGC模型的高保真度。
英文摘要
Graph Neural Networks (GNNs) have achieved remarkable performance in node classification tasks, motivating growing interest in methods capable of explaining their predictions. Recent logic-based approaches, such as LogicXGNN, derive global logical rules for Graph Neural Networks (GNNs) from collections of explanatory subgraphs. While informative, these subgraphs may contain redundant structural information that is specific to individual nodes, potentially limiting the generality of the extracted rules. In this work, we propose a logic-based framework for node classification in Simple Graph Convolution (SGC) networks that uses minimal abductive explanations as an intermediate representation for rule extraction. For each node, we compute a minimal set of node-feature pairs sufficient to preserve the predicted class. These explanations are then used to train decision trees from which global logical rules are extracted. Experiments on benchmark datasets show that the proposed framework produces compact global rules while maintaining high fidelity to the original SGC model.
