Graph Neural Networks (GNNs) perform well in community detection and molecule classification. Counterfactual Explanations (CE) provide counter-examples to overcome the transparency limitations of black-box models. Due to the growing attention in graph learning, we focus on the concepts of CE for GNNs. We analysed the SoA to provide a taxonomy, a uniform notation, and the benchmarking datasets and evaluation metrics. We discuss fourteen methods, their evaluation protocols, twenty-two datasets, and nineteen metrics. We integrated the majority of methods into the GRETEL library to conduct an empirical evaluation to understand their strengths and pitfalls. We highlight open challenges and future work.

A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation / Prado-Romero, M. A.; Prenkaj, B.; Stilo, G.; Giannotti, F.. - In: ACM COMPUTING SURVEYS. - ISSN 0360-0300. - 56:7(2024), pp. 1-37. [10.1145/3618105]

A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation

Prenkaj, B.
Secondo
Formal Analysis
;
Stilo, G.
Penultimo
Formal Analysis
;
Giannotti, F.
Ultimo
Writing – Review & Editing
2024

Abstract

Graph Neural Networks (GNNs) perform well in community detection and molecule classification. Counterfactual Explanations (CE) provide counter-examples to overcome the transparency limitations of black-box models. Due to the growing attention in graph learning, we focus on the concepts of CE for GNNs. We analysed the SoA to provide a taxonomy, a uniform notation, and the benchmarking datasets and evaluation metrics. We discuss fourteen methods, their evaluation protocols, twenty-two datasets, and nineteen metrics. We integrated the majority of methods into the GRETEL library to conduct an empirical evaluation to understand their strengths and pitfalls. We highlight open challenges and future work.
2024
black box problem; counterfactual explainability; Explainability; explainable AI; fairness in AI; graph learning; graph neural networks; graphs; machine learning; molecular recourse; post-hoc explanation
01 Pubblicazione su rivista::01a Articolo in rivista
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation / Prado-Romero, M. A.; Prenkaj, B.; Stilo, G.; Giannotti, F.. - In: ACM COMPUTING SURVEYS. - ISSN 0360-0300. - 56:7(2024), pp. 1-37. [10.1145/3618105]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1723539
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