2 papers
cs.LG2026
A Comparative Study of Counterfactual Explainers for Graph Neural Networks Enabling Multiple Types of Graph Edit
Maria Myrto Villia, Filippos Gouidis, Theodore Patkos +1
Counterfactual explanations for graph-structured data seek to determine minimal and realistic modifications required in an input graph to alter a model's prediction to a predefined…
cs.LG2026
A Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networks
Maria Myrto Villia, Filippos Gouidis, Theodore Patkos +1
In this study, we propose a novel pipeline for generic, model-agnostic, local-level counterfactual explainability in graph neural networks (GNNs). Although counterfactual explainer…