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

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 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 a 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.