为图神经网络解释提供可信度评分，提升解释可靠性。
Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
- 基于图信息瓶颈理论设计可信度评估模块。
- 在分布外数据上验证解释的可靠性和鲁棒性。
- 适合需要信任GNN决策过程的研究者与工程师。
解释图神经网络（GNN）受到广泛关注，因其可提升黑箱模型的可解释性，帮助用户理解模型行为并提取预测中的有价值洞见。尽管已有大量后处理实例级解释方法被提出，但这些解释在分布外或未知测试数据上的可靠性仍不确定。本文提出一种基于理论框架的解释器框架ConfExplainer，其核心是广义图信息瓶颈带置信度约束（GIB-CC），能量化生成解释的可信度。实验结果表明，该方法显著优于现有方法，证明置信度评分对增强GNN解释的可信度和鲁棒性具有关键作用。
原文摘要 · Abstract (English)
Explaining Graph Neural Networks (GNNs) has garnered significant attention due to the need for interpretability, enabling users to understand the behavior of these black-box models better and extract valuable insights from their predictions. While numerous post-hoc instance-level explanation methods have been proposed to interpret GNN predictions, the reliability of these explanations remains uncertain, particularly in the out-of-distribution or unknown test datasets. In this paper, we address this challenge by introducing an explainer framework with the confidence scoring module ( ConfExplainer), grounded in theoretical principle, which is generalized graph information bottleneck with confidence constraint (GIB-CC), that quantifies the reliability of generated explanations. Experimental results demonstrate the superiority of our approach, highlighting the effectiveness of the confidence score in enhancing the trustworthiness and robustness of GNN explanations.
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