mlsanityData to 7 Oct 2026

Relaxed Equivariant Graph Neural Networks

arXiv 2407.20471 ↗First posted 30 Jul 2024cs.LGcited by 7

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Abstract (arXiv)

3D Euclidean symmetry equivariant neural networks have demonstrated notable success in modeling complex physical systems. We introduce a framework for relaxed E(3) graph equivariant neural networks that can learn and represent symmetry breaking within continuous groups. Building on the existing e3nn framework, we propose the use of relaxed weights to allow for controlled symmetry breaking. We show empirically that these relaxed weights learn the correct amount of symmetry breaking.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 30 Jul 2024.

Builds on

11 of 20 references.

References · 11

  1. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  2. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  3. 20183D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Datacited by 348
  4. 2016Steerable CNNscited by 298
  5. 2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 201
  6. 2022e3nn: Euclidean Neural Networkscited by 171
  7. 2022Approximately Equivariant Networks for Imperfectly Symmetric Dynamicscited by 95
  8. 2018N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentialscited by 42
  9. 2020Finding Symmetry Breaking Order Parameters with Euclidean Neural Networkscited by 30
  10. 2023Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolutioncited by 19
  11. 2023Almost Equivariance via Lie Algebra Convolutionscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 5.

7 papers.

  1. 2025Partially Equivariant Reinforcement Learning in Symmetry-Breaking Environmentscited by 3
  2. 2026Approximate Equivariance via Projection-based Regularisationcited by 2
  3. 2025PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARLcited by 1
  4. 2026Quasi-Equivariant Metanetworkscited by 1
  5. 2026Tunable Soft Equivariance with Guaranteescited by 0
  6. 2026Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphscited by 0
  7. 2026Equivariance Breaks the Learning Ratecited by 0