标题： 你的解释可靠吗：图神经网络上的置信度感知解释 显示英文标题

标题： Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks

摘要： 由于需要可解释性以更好地理解这些黑箱模型的行为并从其预测中提取有价值的信息，图神经网络（GNN）的解释引起了广泛关注。虽然已经提出了许多后验实例级解释方法来解读 GNN 的预测，但在分布外或未知测试数据集上，这些解释的可靠性仍然不确定。 本文通过引入一个带有置信评分模块的解释器框架（ConfExplainer）解决了这一挑战，该框架基于理论原理，即具有置信约束的广义图信息瓶颈（GIB-CC），用于量化生成解释的可靠性。实验结果证明了我们方法的优越性，并突显了置信评分在增强 GNN 解释可信度和鲁棒性方面的有效性。 显示英文摘要

摘要： 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.