D4Explainer: In-Distribution GNN Explanations via Discrete Denoising
  Diffusion
Jialin Chen Shirley Wu Abhijit Gupta Rex Ying
The widespread deployment of Graph Neural Networks (GNNs) sparks significant
interest in their explainability, which plays a vital role in model auditing
and ensuring trustworthy graph learning. The objective of GNN explainability is
to discern the underlying graph structures that have the most significant
impact on model predictions. Ensuring that explanations generated are reliable
necessitates consideration of the in-distribution property, particularly due to
the vulnerability of GNNs to out-of-distribution data. Unfortunately,
prevailing explainability methods tend to constrain the generated explanations
to the structure of the original graph, thereby downplaying the significance of
the in-distribution property and resulting in explanations that lack
reliability. To address these challenges, we propose D4Explainer, a novel
approach that provides in-distribution GNN explanations for both counterfactual
and model-level explanation scenarios. The proposed D4Explainer incorporates
generative graph distribution learning into the optimization objective, which
accomplishes two goals: 1) generate a collection of diverse counterfactual
graphs that conform to the in-distribution property for a given instance, and
2) identify the most discriminative graph patterns that contribute to a
specific class prediction, thus serving as model-level explanations. It is
worth mentioning that D4Explainer is the first unified framework that combines
both counterfactual and model-level explanations. Empirical evaluations
conducted on synthetic and real-world datasets provide compelling evidence of
the state-of-the-art performance achieved by D4Explainer in terms of
explanation accuracy, faithfulness, diversity, and robustness.