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Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks (ICML 2023)
| Date | Stars |
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| 2026-07-31 | 525 |
| 2026-08-01 | 525 |
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| 2026-08-06 | 525 |
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# ⚔️ Geometric GNN Dojo *Geometric GNN Dojo* is a pedagogical resource for beginners and experts to explore the design space of **Graph Neural Networks for geometric graphs**. Check out the accompanying paper ['On the Expressive Power of Geometric Graph Neural Networks'](https://arxiv.org/abs/2301.09308), which studies the expressivity and theoretical limits of geometric GNNs. > Chaitanya K. Joshi*, Cristian Bodnar*, Simon V. Mathis, Taco Cohen, and Pietro Liò. On the Expressive Power of Geometric Graph Neural Networks. *International Conference on Machine Learning*. > >[PDF](https://arxiv.org/pdf/2301.09308.pdf) | [Slides](https://www.chaitjo.com/publication/joshi-2023-expressive/Geometric_GNNs_Slides.pdf) | [Video](https://youtu.be/5ulJMtpiKGc) ❓**New to geometric GNNs:** try our practical notebook on [*Geometric GNNs 101*](geometric_gnn_101.ipynb), prepared for MPhil students at the University of Cambridge. <a target="_blank" href="https://colab.research.google.com/github/chaitjo/geometric-gnn-dojo/blob/main/geometric_gnn_101.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab (recommended!)"/> </a> ## Architectures The `/models` directory provides unified implementations of several popular geometric GNN architectures: - Invariant GNNs: [SchNet](https://arxiv.org/abs/1706.08566), [DimeNet](https://arxiv.org/abs/2003.03123), [SphereNet](https://arxiv.org/abs/2102.05013) - Equivariant GNNs using cartesian vectors: [E(n) Equivariant GNN](https://proceedings.mlr.press/v139/satorras21a.html), [GVP-GNN](https://arxiv.org/abs/2009.01411) - Equivariant GNNs using spherical tensors: [Tensor Field Network](https://arxiv.org/abs/1802.08219), [MACE](http://arxiv.org/abs/2206.07697) - 🔥 Your new geometric GNN architecture? <figure><center><img src="experiments/fig/axes-of-expressivity.png" width="70%"></center></figure> ## Experiments The `/experiments` directory contains notebooks with synthetic experiments to highlight practical challenges in building powerful geometric GNNs: - `kchains.ipynb`: Distinguishing k-chains, which test a model's ability to **propagate geometric information** non-locally and demonstrate oversquashing with increased depth/longer chains. - `rotsym.ipynb`: Rotationally symmetric structures, which test a layer's ability to **identify neighbourhood orientation** and highlight the utility of higher order tensors in equivariant GNNs. - `incompleteness.ipynb`: Counterexamples from [Pozdnyakov et al.](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.166001), which test a layer's ability to create **distinguishing fingerprints for local neighbourhoods** and highlight the need for higher body order of local scalarisation (distances, angles, and beyond). ## Installation ```bash # Create new conda environment conda create --prefix ./env python=3.8 conda activate ./env # Install PyTorch (Check CUDA version for GPU!) # # Option 1: CPU conda install pytorch==1.12.0 -c pytorch # # Option 2: GPU, CUDA 11.3 # conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch # Install dependencies conda install matplotlib pandas networkx conda install jupyterlab -c conda-forge pip install e3nn==0.4.4 ipdb ase # Install PyG (Check CPU/GPU/MacOS) # # Option 1: CPU, MacOS pip install torch-scatter torch-sparse torch-cluster torch-spline-conv -f https://data.pyg.org/whl/torch-1.12.0+cpu.html pip install torch-geometric # # Option 2: GPU, CUDA 11.3 # pip install torch-scatter torch-sparse torch-cluster torch-spline-conv -f https://data.pyg.org/whl/torch-1.12.1+cu113.html # pip install torch-geometric # # Option 3: CPU/GPU, but may not work on MacOS # conda install pyg -c pyg ``` ## Directory Structure and Usage ``` . ├── README.md | ├── geometric_gnn_101.ipynb # A gentle introduction to Geometric GNNs | ├── experiments # Synthetic experiments | | │ ├── kchains.ipynb
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