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Maria Myrto Villia

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#machine learning Preprint Sep 2026

A Comparative Study of Counterfactual Explainers for Graph Neural Networks Enabling Multiple Types of Graph Edit

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 output. Although counterfactual explainers that support modifying the graph by both adding and removing edges have recently emerged, there...

Maria Myrto Villia, Filippos Gouidis, T. Patkos et al. · 0 citations

2026

A framework for Counterfactual Explainability in Graph Neural Networks

The approach combines ideas from factual explainability with edge prediction models inspired by link prediction to enhance the quality, robustness, and interpretability of counterfactual explanations while keeping the method computationally tractable.

Maria Myrto Villia · KR Doctoral Consortium · 0 citations