提出可解释图神经网络的新框架，让解释更真实可靠。
LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks
- 用逻辑规则显式建模消息传递机制，确保解释有数据依据
- 新指标Fid_D提升20%以上，且推理速度加快10-100倍
- 适合关注模型可信解释的研究者和工业应用落地者
现有基于规则的图神经网络解释方法虽具全局可解释性，但通常在不可解释的概念空间中优化和评估保真度，忽略了最终子图解释对用户的真实可理解性。这导致解释看似准确，实则不可靠。为此，我们提出LogicXGNN，一种后处理框架，通过构建覆盖可靠谓词的逻辑规则，显式捕捉GNN的消息传递结构，从而保障解释的有效接地。我们还引入数据接地保真度（Fid_D），在最终图形式下评估解释，辅以覆盖率和有效性等指标。大量实验表明，LogicXGNN在平均上将Fid_D提升超过20%，且比当前最优方法快10-100倍。其强可扩展性和优异实用性使生成的解释既忠实于模型逻辑，又可靠地扎根于可观测数据。
原文摘要 · Abstract (English)
Existing rule-based explanations for Graph Neural Networks (GNNs) provide global interpretability but often optimize and assess fidelity in an intermediate, uninterpretable concept space, overlooking grounding quality for end users in the final subgraph explanations. This gap yields explanations that may appear faithful yet be unreliable in practice. To this end, we propose LogicXGNN, a post-hoc framework that constructs logical rules over reliable predicates explicitly designed to capture the GNN's message-passing structure, thereby ensuring effective grounding. We further introduce data-grounded fidelity ($\textit{Fid}_{\mathcal{D}}$), a realistic metric that evaluates explanations in their final-graph form, along with complementary utility metrics such as coverage and validity. Across extensive experiments, LogicXGNN improves $\textit{Fid}_{\mathcal{D}}$ by over 20% on average relative to state-of-the-art methods while being 10-100 $\times$ faster. With strong scalability and utility performance, LogicXGNN produces explanations that are faithful to the model's logic and reliably grounded in observable data. Our code is available at https://github.com/allengeng123/LogicXGNN/.
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