On the Ability of Graph Neural Networks to Model Interactions Between Vertices

@article{Razin2022OnTA,
  title={On the Ability of Graph Neural Networks to Model Interactions Between Vertices},
  author={Noam Razin and Tom Verbin and Nadav Cohen},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.16494}
}
Graph neural networks (GNNs) are widely used for modeling complex interactions between entities represented as vertices of a graph. Despite recent efforts to theoretically analyze the expressive power of GNNs, a formal characterization of their ability to model interactions is lacking. The current paper aims to address this gap. Formalizing strength of interactions through an established measure known as separation rank , we quantify the ability of certain GNNs to model interaction between a… 
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