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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2210.12089 |
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| _version_ | 1866917690263732224 |
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| author | Prado-Romero, Mario Alfonso Prenkaj, Bardh Stilo, Giovanni Giannotti, Fosca |
| author_facet | Prado-Romero, Mario Alfonso Prenkaj, Bardh Stilo, Giovanni Giannotti, Fosca |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_12089 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges Prado-Romero, Mario Alfonso Prenkaj, Bardh Stilo, Giovanni Giannotti, Fosca Machine Learning Artificial Intelligence 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. |
| title | A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2210.12089 |