Mathematical expressiveness of graph neural networks

HIGHLIGHTS

• who: Guillaume Lachaud and collaborators from the ISEP-Institut Supu00e9rieur d`u00c9lectronique de Paris, Paris, France have published the article: Mathematical Expressiveness of Graph Neural Networks, in the Journal: Mathematics 2022, 0 of /2022/
• what: Specifically, the authors provide templates for the most widely used architectures, and the authors survey the major mathematical results regarding their expressiveness, from obtaining the most expressive architectures using higher order networks and invariant networks to finding computationally tractable and powerful architectures. The authors present an overview of the approaches used to improve the expressiveness of GNNs. The early works . . .
  

   

  

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