Decidability of graph neural networks via logical characterizations
We present results concerning the expressiveness and decidability of a popular graph learning formalism, graph neural networks (GNNs), exploiting connections with logic. We use a family of recently-discovered decidable logics involving ``Presburger quantifiers''. We show how to use the...
| Главные авторы: | , , , |
|---|---|
| Формат: | Conference item |
| Язык: | English |
| Опубликовано: |
Lipics
2024
|