https://github.com/senya-ashukha/simple-equivariant-gnn

A short and easy PyTorch implementation of E(n) Equivariant Graph Neural Networks
https://github.com/senya-ashukha/simple-equivariant-gnn

deep-learning deep-neural-networks egnn equivariance gnn graph-neural-networks homo molecules neural-networks pytorch qm9

Last synced: 8 days ago
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A short and easy PyTorch implementation of E(n) Equivariant Graph Neural Networks

• Host: GitHub
• URL: https://github.com/senya-ashukha/simple-equivariant-gnn
• Owner: senya-ashukha
• License: apache-2.0
• Created: 2022-01-13T10:38:16.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-01-14T12:25:53.000Z (over 3 years ago)
• Last Synced: 2025-04-02T14:02:14.594Z (about 2 months ago)
• Topics: deep-learning, deep-neural-networks, egnn, equivariance, gnn, graph-neural-networks, homo, molecules, neural-networks, pytorch, qm9
• Language: Python
• Homepage:
• Size: 193 KB
• Stars: 130
• Watchers: 3
• Forks: 12
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Simple implementation of Equivariant GNN

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/senya-ashukha/simple-equivariant-gnn/blob/main/simple-egnn.ipynb)

- A short implementation of [E(n) Equivariant Graph Neural Networks](https://arxiv.org/pdf/2102.09844.pdf) for [HOMO energy](https://en.wikipedia.org/wiki/HOMO_and_LUMO) prediction.

- Just 50 lines of code; 

- The implementation is based on pure PyTorch & Numpy, it has no external packages (like PyTorch-geometric).

- Closely matches the Mean Absolute Error reported in the paper.