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E(n) Equivariant Graph Neural Networks
Víctor Garcia Satorras · Emiel Hoogeboom · Max Welling

Tue Jul 20 07:25 AM -- 07:30 AM (PDT) @ None

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.

Author Information

Víctor Garcia Satorras (University of Amsterdam)
Emiel Hoogeboom (University of Amsterdam)
Max Welling (University of Amsterdam & Qualcomm)

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