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E(n) Equivariant Graph Neural Networks

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arxiv 2102.09844 v3 pith:FERTZIGU submitted 2021-02-19 cs.LG stat.ML

Victor Garcia Satorras , Emiel Hoogeboom , Max Welling This is my paper
classification cs.LGstat.ML
keywords graphequivariantnetworksneuralexistingmethodsmodelspaces
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 105 citations worldwide. Full citation record

  1. Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics

    cs.LG 2026-04 conditional novelty 6.5 of 10

    Structure-pretrained diffusion plus an equivariant temporal interpolator generates chemically realistic MD trajectories on small molecules, tetrapeptides, and proteins by separating spatial and temporal learning.

  2. Graph Neural Network Predictions of Carbon 1s Binding Energies with Near-Experimental Accuracy

    physics.chem-ph 2026-04 unverdicted novelty 6.0 of 10

    Graph neural network achieves 0.33 eV MAE predicting experimental C 1s core-electron binding energies with demonstrated size transferability and E(3)-equivariance benefits.

  3. InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames

    cs.LG 2025-10 conditional novelty 6.0 of 10

    An autoregressive transformer with inertial-frame tokenization and geometric rotary positional encoding reports state-of-the-art validity and stability on QM9, GEOM-Drugs, and B3LYP, plus strong functional-group-condi...

  4. GeoAda: Efficiently Finetune Geometric Diffusion Models with Equivariant Adapters

    cs.LG 2025-07 conditional novelty 6.0 of 10

    GeoAda uses SE(3)-equivariant adapter blocks with zero-initialized convolutions to fine-tune frozen geometric diffusion models for new control tasks with few parameters.

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