Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
This addresses the need for reliable interpretability in GNNs, particularly for users dealing with uncertain or unknown data, though it is incremental as it builds on existing explanation methods.
The paper tackles the problem of unreliable explanations for Graph Neural Networks (GNNs) in out-of-distribution scenarios by introducing ConfExplainer, a framework with a confidence scoring module based on a generalized graph information bottleneck with confidence constraint, which quantifies explanation reliability and enhances trustworthiness and robustness.
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.