A Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networks

This study introduces a model-agnostic framework for local-level counterfactual explainability in graph neural networks (GNNs), addressing the limitations of existing explainers that only add or remove edges. The proposed method integrates advancements in factual explainability with link prediction techniques to improve the robustness and intuitiveness of explanations. Experimental results on both real-world and synthetic graph classification benchmarks indicate significant improvements over state-of-the-art methods, which is crucial for practitioners seeking effective interpretability tools in GNN applications.