Pytorch and GNNs tutorial

Hello there! In this repo you will find a tutorial on how to use Pytorch and Graph Neural Networks (GNN) for regression problems. This tutorial will be given by me in the Artificial Intelligence in Astronomy (AIA) Seminars.

If you have any questions, complains, suggestions or you just wanna chat do not hesitate to contact me:

• e-mail: [email protected]
• github: natalidesanti

Material

Here you can find:

• Slides for the presentations
• Jupyter-notebooks (with the codes)

Supplementary material

If you wanna learn more about GNNs you can check amazing materials as the ones listed below:

• PyTorch tutorials, by PyTorch team, by PyTorch Geometric team;
• PyTorch Geometric tutorials
• Graph Neural Networks to predict the cosmological parameters or the galaxy power spectrum from galaxy catalogs, by Pablo Villanueva-Domingo;
• Graph Neural Networks Tutorial, by Christian Kragh Jespersen.

You can see amazing scientific publications using GNNs such as:

• Massara, E., Villaescusa-Navarro, F., & Percival, W. J. 2023, arXiv e-prints, arXiv: 2309.05850;
• de Santi, N. S. M., Shao, H., Villaescusa-Navarro, F., et al. 2023, ApJ, 952, 69, doi: 10.3847/1538-4357/acd1e2;
• Chen, Z., Mao, H., Li, H., et al. 2023, arXiv: 2307.03393;
• Shao, H., de Santi, N. S. M., Villaescusa-Navarro, F., et al. 2023, arXiv e-prints, arXiv: 2302.14591;
• Villanueva-Domingo, P., & Villaescusa-Navarro, F. 2022, ApJ, 937, 115, doi: 10.3847/1538-4357/ac8930;
• Shao, H., Villaescusa-Navarro, F., Villanueva-Domingo, P., et al. 2022b, arXiv e-prints, arXiv: 2209.06843;
• Villanueva-Domingo, P., Villaescusa-Navarro, F., Genel, S., et al. 2021, arXiv: 2111.14874;
• Villanueva-Domingo, P., Villaescusa-Navarro, F., Anglés-Alcázar, D., et al. 2022, ApJ, 935, 30. doi: 10.3847/1538-4357/ac7aa3;
• Makinen, T. L., Charnock, T., Lemos, P., et al. 2022, arXiv e-prints, arXiv: (2207.05202)[https://arxiv.org/abs/2207.05202];
• Anagnostidis, S., Thomsen, A., Kacprzak, T., et al. 2022, arXiv e-prints, arXiv: (2211.12346)[https://arxiv.org/abs/2211.12346];
• Wang, N., Zhang, Y., Li, Z., et al. 2018, arXiv e-prints, arXiv: 1804.01654.