Developing and Evaluating Graph Counterfactual Explanations with


Mario Alfonso Prado-Romero, Bardh Prenkaj and Giovanni Stilo

Counterfactual Explanations

Answer the question: “how should an input instance be perturbed to obtain a desired predicted label?"


Provide recourse to the users via feedback they can act upon to change the prediction result in their favor.


Help to indetify bias and increase fairness.

GCE Literature Limitations

No well-established datasets


No standard evaluation metrics


Distinct oracles, built on different frameworks, are used for the same datasets


Doesn’t compare exhaustively with other state-of-the-art methods


Framework for evaluating and developing GCE methods

Open source with modular and extensible design

Integrates state-of-the-art datasets, oracles, explainers and evaluation metrics


Live Demo