• Corpus ID: 231979049

E(n) Equivariant Graph Neural Networks

@inproceedings{Satorras2021EnEG,
  title={E(n) Equivariant Graph Neural Networks},
  author={Victor Garcia Satorras and Emiel Hoogeboom and Max Welling},
  booktitle={International Conference on Machine Learning},
  year={2021}
}
This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E( n ) Equivariant Graph Neural Networks (EGNNs). In contrast with existing methods, our work does not require computationally expensive higher-order representations in intermediate layers while it still achieves competitive or better performance. In addition, whereas existing methods are limited to equivariance on 3 dimensional spaces, our model is easily… 

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