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arXiv · 2102.09844

E(n) Equivariant Graph Neural Networks

Victor Garcia Satorras·Emiel Hoogeboom·Max Welling
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Abstract

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 scaled to higher-dimensional spaces. We demonstrate the effectiveness of our method on dynamical systems modelling, representation learning in graph autoencoders and predicting molecular properties.

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BibTeXRIS

Victor Garcia Satorras, Emiel Hoogeboom, Max Welling. 2021-02-19. E(n) Equivariant Graph Neural Networks. https://arxiv.org/abs/2102.09844

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