mlsanityData to 7 Oct 2026

Beyond permutation equivariance in graph networks

arXiv 2103.14066 ↗First posted 25 Mar 2021cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

In this draft paper, we introduce a novel architecture for graph networks which is equivariant to the Euclidean group in n-dimensions. The model is designed to work with graph networks in their general form and can be shown to include particular variants as special cases. Thanks to its equivariance properties, we expect the proposed model to be more data efficient with respect to classical graph architectures and also intrinsically equipped with a better inductive bias. We defer investigating this matter to future work.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 30 references that are in the map. First posted on arXiv 25 Mar 2021.

Builds on

21 of 30 references.

References · 21

  1. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  3. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  4. 2016Group Equivariant Convolutional Networkscited by 1,154
  5. 2021E(n) Equivariant Graph Neural Networkscited by 951
  6. 2020Learning to Simulate Complex Physics with Graph Networkscited by 867
  7. 2020Learning Mesh-Based Simulation with Graph Networkscited by 840
  8. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  9. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  10. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
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  12. 2020Discovering Symbolic Models from Deep Learning with Inductive Biasescited by 335
9 more
  1. 2020Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Datacited by 248
  2. 2020Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densitiescited by 248
  3. 2020Lorentz Group Equivariant Neural Network for Particle Physicscited by 120
  4. 2020Causal Discovery in Physical Systems from Videoscited by 77
  5. 2020Group Equivariant Stand-Alone Self-Attention For Visioncited by 63
  6. 2019Physical Symmetries Embedded in Neural Networkscited by 40
  7. 2021Autobahn: Automorphism-based Graph Neural Netscited by 39
  8. 2020Isometric Transformation Invariant and Equivariant Graph Convolutional Networkscited by 17
  9. 2021Symmetry meets AIcited by 13

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Cited by year

Citing papers per year: 2021: 2.

2 papers.

  1. 2021Symmetry-driven graph neural networkscited by 1
  2. 2021Encoding Involutory Invariances in Neural Networkscited by 1