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Evaluating explainability for graph neural networks

As explanations are increasingly used to understand the behavior of graph neural networks (GNNs), evaluating the quality and reliability of GNN explanations is crucial. However, assessing the quality of GNN explanations is challenging as existing graph datasets have no or unreliable ground-truth exp...

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Detalles Bibliográficos
Autores principales: Agarwal, Chirag, Queen, Owen, Lakkaraju, Himabindu, Zitnik, Marinka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10024712/
https://www.ncbi.nlm.nih.gov/pubmed/36934095
http://dx.doi.org/10.1038/s41597-023-01974-x

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