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Paolo Baracco

1 paper indexed

arxivcs.LG2026-07-06

Measuring What Matters: A Unified Evaluation Framework for GNN Explainability

Francesco Paolo Nerini, Mirko Zaffaroni, Paolo Baracco, Gabriele Ciravegna, Alan Perotti

Graph eXplainable AI (G-XAI) is increasingly important for making Graph Neural Networks interpretable and accountable. While a growing number of explainers are available, choosing the right method and assessing the trustworthiness of its outputs remains unclear. Consistent evalua…

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