LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908890328727552 |
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| author | Geng, Chuqin Zhao, Ziyu Wang, Zhaoyue Ye, Haolin Jiang, Yuhe Si, Xujie |
| author_facet | Geng, Chuqin Zhao, Ziyu Wang, Zhaoyue Ye, Haolin Jiang, Yuhe Si, Xujie |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19476 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks Geng, Chuqin Zhao, Ziyu Wang, Zhaoyue Ye, Haolin Jiang, Yuhe Si, Xujie Machine Learning 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/. |
| title | LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.19476 |