• MSc thesis project in Engineering in Computer Science, Sapienza University of Rome.

CFPGExplainer (CounterFactual Parameterized Graph neural network Explainer) is a counterfactual explainer model for the node classification task in Graph Neural Networks (GNNs). The proposed framework expands on the idea of perturbation-based approaches to achieve, at once, commonly desired properties for GNN explainers, such as model-level explanations (i.e. not tailored to a single prediction instance), a more efficient inference process and counterfactual examples for each generated explanation.