Revolutionizing GNN Explanations: Attack-Informed Counterfactuals

This research introduces ATEX-CF, a novel approach to enhance the reliability of explanations for Graph Neural Networks (GNNs). By integrating attack signals into the counterfactual generation process, the work promises to improve explanation stability and alignment with vulnerable decision regions. This is a significant advancement in making AI models more transparent and trustworthy.