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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated list of topological deep learning (TDL) resources and links.
| Date | Stars |
|---|---|
| 2026-07-31 | 293 |
| 2026-08-02 | 294 |
| 2026-08-06 | 296 |
Today
+2 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Awesome TDL [](https://github.com/sindresorhus/awesome) [](http://makeapullrequest.com) 
A curated list of Topological Deep Learning (TDL) tools and resources.
<p align="center">
<img width="500" src="https://nyuad.nyu.edu/content/nyuad/en/home/research/faculty-labs-and-projects/cqts/events/_jcr_content/mainparsys/tabs_1957616313/tabparsys1/image/image.img.jpg">
</p>
1. **Simplicial Neural Networks**. Stefania Ebli, Michaël Defferrard, Gard Spreemann. *NeurIPS 2020 Workshop TDA and Beyond*. [Paper](https://openreview.net/pdf?id=nPCt39DVIfk), [Code](https://github.com/stefaniaebli/simplicial_neural_networks) <img alt="PyTorch" src="https://cdn.simpleicons.org/pytorch/EE4C2C" height="14" style="vertical-align:text-bottom">, <a href="https://www.youtube.com/watch?v=2cidzWXH_vg"><img alt="YouTube" src="https://cdn.simpleicons.org/youtube/FF0000" height="14" style="vertical-align:text-bottom"></a>
2. **Simplicial 2-Complex Convolutional Neural Nets**. Eric Bunch, Qian You, Glenn Fung, Vikas Singh. *NeurIPS 2020 Workshop TDA and Beyond*. [Paper](https://openreview.net/pdf?id=TLbnsKrt6J-), [Code](https://github.com/AmFamMLTeam/simplicial-2-complex-cnns) <img alt="PyTorch" src="https://cdn.simpleicons.org/pytorch/EE4C2C" height="14" style="vertical-align:text-bottom">, <a href="https://www.youtube.com/watch?v=2cidzWXH_vg">
3. **Cell complex neural networks**. Mustafa Hajij, Kyle Istvan, and Ghada Zamzmi. *NeurIPS Workshop on Topological Data Analysis and Beyond, 2020*. [Paper](https://openreview.net/pdf?id=6Tq18ySFpGU), <a href="https://www.youtube.com/watch?v=WIlLlKoTU9o"> <img src="https://1000marken.net/wp-content/uploads/2021/01/Youtube-logo-2015.png" alt="YouTube Video" width="46" height="15"> </a>
4. **Principled simplicial neural networks for trajectory prediction**. Roddenberry, T. Mitchell, Nicholas Glaze, and Santiago Segarra. *ICML 2021*. [Paper](http://proceedings.mlr.press/v139/roddenberry21a/roddenberry21a.pdf), [Code](https://github.com/nglaze00/SCoNe_GCN) <img width="20" height="11" src="https://production-assets.paperswithcode.com/perf/images/frameworks/jax-6ee30fa5.png">
5. **Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks**. Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montúfar, Pietro Liò, Michael Bronstein. *ICML 2021*. [Paper](http://proceedings.mlr.press/v139/bodnar21a/bodnar21a.pdf), [Code](https://github.com/twitter-research/cwn) <img alt="PyTorch" src="https://cdn.simpleicons.org/pytorch/EE4C2C" height="14" style="vertical-align:text-bottom"> <a href="https://www.youtube.com/watch?v=wACDSoDNTfE">
<img src="https://1000marken.net/wp-content/uploads/2021/01/Youtube-logo-2015.png" alt="YouTube Video" width="46" height="15">
</a>
6. **Weisfeiler and Lehman Go Cellular: CW Networks**. Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yu Guang Wang, Pietro Liò, Guido Montúfar, Michael Bronstein. *NeurIPS 2021*. [Paper](https://proceedings.neurips.cc/paper/2021/file/157792e4abb490f99dbd738483e0d2d4-Paper.pdf), [Code](https://github.com/twitter-research/cwn) <img alt="PyTorch" src="https://cdn.simpleicons.org/pytorch/EE4C2C" height="14" style="vertical-align:text-bottom"> <a href="https://www.youtube.com/watch?v=MTQGNVTn9lQ">
<img src="https://1000marken.net/wp-content/uploads/2021/01/Youtube-logo-2015.png" alt="YouTube Video" width="46" height="15">
</a>
7. **Simplicial Attention Neural Networks**. Lorenzo Giusti, Claudio Battiloro, Paolo Di Lorenzo, Stefania Sardellitti, Sergio Barbarossa. *arXiv 2022*. [Paper](https://arxiv.org/abs/2203.07485), [Code](https://github.com/lrnzgiusti/simplicial-attention-networks) <img width="46" height="11" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbfExcerpt of 24,951 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8c4e49c0c21a3caa, topic:deep-learning