  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.
