用最小反事实解释生成简洁逻辑规则，提升SGC模型可解释性
From Abductive Explanations to Global Logical Rules for Node Classification in SGCs
- 以最小反事实解释为中间表示，提取节点特征对分类的关键作用
- 在多个基准数据集上生成的规则紧凑且保持95%以上原模型准确率
- 适合需要透明决策过程的图学习应用，如医疗诊断与金融风控
图神经网络（GNN）在节点分类任务中表现优异，推动了对其预测结果可解释性的研究。现有基于逻辑的方法（如LogicXGNN）从解释子图中提取全局逻辑规则，但这些子图常包含与特定节点相关的冗余结构信息，影响规则通用性。本文提出一种基于逻辑的框架，用于简化图卷积网络（SGC）的节点分类可解释性分析。针对每个节点，计算一组最少的节点-特征对，足以维持其预测类别；这些最小反事实解释被用于训练决策树，进而提取全局逻辑规则。实验表明，该框架在多个基准数据集上生成的规则更紧凑，同时保持对原始SGC模型高达95%以上的预测一致性。
原文摘要 · Abstract (English)
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.
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