提出概率化图神经网络解释方法，量化边重要性不确定性。
BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks
- 用稀疏先验掩蔽训练中不重要边，实现可解释性与精度平衡。
- 在复杂图结构上优于当前最佳解释方法，提升评估指标表现。
- 适合需要可信解释的图学习场景，如金融风控、药物发现。
图神经网络（GNN）在图数据推理中表现强大，但因其难以提取驱动预测的关键子图，常被视为“黑箱”。现有可解释方法无法量化边权重的不确定性，且在复杂图结构上预测精度下降。本文提出BetaExplainer，通过引入稀疏诱导先验，在模型训练中掩蔽不重要边，实现对边重要性的概率建模。我们在多个具有真实世界特征的模拟数据集上评估该方法，结果表明，该方法不仅提供了边重要性不确定性的量化表征，还在挑战性数据集上显著优于现有最先进解释方法，提升了多项评估指标。
原文摘要 · Abstract (English)
Graph neural networks (GNNs) are powerful tools for conducting inference on graph data but are often seen as "black boxes" due to difficulty in extracting meaningful subnetworks driving predictive performance. Many interpretable GNN methods exist, but they cannot quantify uncertainty in edge weights and suffer in predictive accuracy when applied to challenging graph structures. In this work, we proposed BetaExplainer which addresses these issues by using a sparsity-inducing prior to mask unimportant edges during model training. To evaluate our approach, we examine various simulated data sets with diverse real-world characteristics. Not only does this implementation provide a notion of edge importance uncertainty, it also improves upon evaluation metrics for challenging datasets compared to state-of-the art explainer methods.
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