Evaluating Neighbor Explainability for Graph Neural Networks
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
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| _version_ | 1866914735204597760 |
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| author | Llorente, Oscar Fawzy, Rana Keown, Jared Horemuz, Michal Vaderna, Péter Laki, Sándor Kotroczó, Roland Csoma, Rita Szalai-Gindl, János Márk |
| author_facet | Llorente, Oscar Fawzy, Rana Keown, Jared Horemuz, Michal Vaderna, Péter Laki, Sándor Kotroczó, Roland Csoma, Rita Szalai-Gindl, János Márk |
| contents | Explainability in Graph Neural Networks (GNNs) is a new field growing in the last few years. In this publication we address the problem of determining how important is each neighbor for the GNN when classifying a node and how to measure the performance for this specific task. To do this, various known explainability methods are reformulated to get the neighbor importance and four new metrics are presented. Our results show that there is almost no difference between the explanations provided by gradient-based techniques in the GNN domain. In addition, many explainability techniques failed to identify important neighbors when GNNs without self-loops are used. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_08118 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Evaluating Neighbor Explainability for Graph Neural Networks Llorente, Oscar Fawzy, Rana Keown, Jared Horemuz, Michal Vaderna, Péter Laki, Sándor Kotroczó, Roland Csoma, Rita Szalai-Gindl, János Márk Machine Learning Artificial Intelligence Explainability in Graph Neural Networks (GNNs) is a new field growing in the last few years. In this publication we address the problem of determining how important is each neighbor for the GNN when classifying a node and how to measure the performance for this specific task. To do this, various known explainability methods are reformulated to get the neighbor importance and four new metrics are presented. Our results show that there is almost no difference between the explanations provided by gradient-based techniques in the GNN domain. In addition, many explainability techniques failed to identify important neighbors when GNNs without self-loops are used. |
| title | Evaluating Neighbor Explainability for Graph Neural Networks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2311.08118 |