Generating Explanations for Conceptual Validation of Graph Neural Networks: An Investigation of Symbolic Predicates Learned on Relevance-Ranked Sub-Graphs

Graph Neural Networks (GNN) show good performance in relational data classification. However, their contribution to concept learning and the validation of their output from an application domain’s and user’s perspective have not been thoroughly studied. We argue that combining symbolic learning meth...

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Detalles Bibliográficos
Autores principales: Finzel, Bettina, Saranti, Anna, Angerschmid, Alessa, Tafler, David, Pfeifer, Bastian, Holzinger, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Technical Contribution
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794543/
https://www.ncbi.nlm.nih.gov/pubmed/36590103
http://dx.doi.org/10.1007/s13218-022-00781-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794543/
https://www.ncbi.nlm.nih.gov/pubmed/36590103
http://dx.doi.org/10.1007/s13218-022-00781-7

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