Building Topological Neural Networks for Topological Deep Learning

Contributing to TMX • References

TopoModelX (TMX) is a Python module for topological deep learning. It offers simple and efficient tools to implement topological neural networks for science and engineering.

TMX's development follows the topological deep learning (TDL) blue print laid out in:

• Hajij et al. 2023. Topological Deep Learning: Going Beyond Graph Data.

TMX can reproduce and extend the topological neural networks (TNNs) surveyed in:

• Papillon et al. 2023. Architectures of Topological Deep Learning: A Survey on Topological Neural Networks.

See our graphical literature review with message-passing equations available at https://github.com/awesome-tnns/awesome-tnns.

Note: TMX is still under development.

🦾 Contributing to TMX

To develop tmx on your machine, here are some tips.

First, we recommend using Python 3.11.3, which is the python version used to run the unit-tests.

For example, create a conda environment:

   conda create -n tmx python=3.11.3
   conda activate tmx

Then:

1. Clone a copy of tmx from source:

   git clone git@github.com:pyt-team/TopoModelX.git
   cd TopoModelX

2. Install tmx in editable mode:

   pip install -e '.[all]'

   Notes:

   • Requires pip >= 21.3. Refer: PEP 660.
   • On Windows, use pip install -e .[all] instead (without quotes around [all]).

3. Install torch, torch-scatter, torch-sparse with or without CUDA depending on your needs.

     pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/${CUDA}
     pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.0.1+${CUDA}.html
     pip install torch-cluster -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.html

   where ${CUDA} should be replaced by either cpu, cu102, cu113, or cu115 depending on your PyTorch installation (torch.version.cuda).

4. Ensure that you have a working tmx installation by running the entire test suite with

   pytest

   In case an error occurs, please first check if all sub-packages (torch-scatter, torch-sparse, torch-cluster and torch-spline-conv) are on its latest reported version.

5. Install pre-commit hooks:

   pre-commit install

🔍 References

To learn more about the topological deep learning blueprint:

• Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy, Tolga Birdal, Tamal K. Dey, Soham Mukherjee, Shreyas N. Samaga, Neal Livesay, Robin Walters, Paul Rosen, Michael T. Schaub. Topological Deep Learning: Going Beyond Graph Data.

  @misc{hajij2023topological,
    title={Topological Deep Learning: Going Beyond Graph Data},
    author={Mustafa Hajij and Ghada Zamzmi and Theodore Papamarkou and Nina Miolane and Aldo Guzmán-Sáenz and Karthikeyan Natesan Ramamurthy and Tolga Birdal and Tamal K. Dey and Soham Mukherjee and Shreyas N. Samaga and Neal Livesay and Robin Walters and Paul Rosen and Michael T. Schaub},
    year={2023},
    eprint={2206.00606},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
  }

  Funding

Partially funded by the European Union (ERC, HIGH-HOPeS, 101039827). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

Partially funded by the National Science Foundation (DMS-2134231, DMS-2134241).