MetaGMT通过元学习提升图神经网络解释的可靠性。
MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration
- 采用双层优化元学习框架，过滤虚假相关性
- 在多个基准上解释性能提升最高达8%
- 适合需要可信解释的医疗金融等高风险场景
图神经网络（GNN）在医疗、金融等高风险领域的应用日益广泛，对其决策过程的可靠解释需求迫切。尽管图多线性网络（GMT）具备内在可解释性，但仍易受虚假相关性影响，削弱信任。本文提出MetaGMT，一种基于元学习的过滤框架，通过双层优化显著提升解释质量与鲁棒性，在BA-2Motifs、MUTAG、SP-Motif基准上均表现优异。该方法在保持分类精度的同时，使解释的AUC-ROC提升最高达8%（在SP-Motif 0.5上），优于基线方法。这些改进有助于模型调试、针对性重训练及人工监督，推动可信GNN系统在真实场景中的安全部署。
原文摘要 · Abstract (English)
The growing adoption of Graph Neural Networks (GNNs) in high-stakes domains like healthcare and finance demands reliable explanations of their decision-making processes. While inherently interpretable GNN architectures like Graph Multi-linear Networks (GMT) have emerged, they remain vulnerable to generating explanations based on spurious correlations, potentially undermining trust in critical applications. We present MetaGMT, a meta-learning framework that enhances explanation fidelity through a novel bi-level optimization approach. We demonstrate that MetaGMT significantly improves both explanation quality (AUC-ROC, Precision@K) and robustness to spurious patterns, across BA-2Motifs, MUTAG, and SP-Motif benchmarks. Our approach maintains competitive classification accuracy while producing more faithful explanations (with an increase up to 8% of Explanation ROC on SP-Motif 0.5) compared to baseline methods. These advancements in interpretability could enable safer deployment of GNNs in sensitive domains by (1) facilitating model debugging through more reliable explanations, (2) supporting targeted retraining when biases are identified, and (3) enabling meaningful human oversight. By addressing the critical challenge of explanation reliability, our work contributes to building more trustworthy and actionable GNN systems for real-world applications.
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