Welcome to the GMLFA (Graph Machine Learning Foundation and Applications) Tutorials!

Course website: Course Webpage
Course edition: GMLFA3 - Autumn 2024, GMLFA2 - Autumn 2023, Being kept up to date
Repository: Github Repository
Recordings: YouTube Playlist
Author: Animesh Sachan (TA-GMLFA)

Note

This Project is currently under developement! If you are interested to contribute, create a pull request of the project github.

For this course, we created a series of Jupyter notebooks that are designed to help you understanding the “theory” from the lectures by seeing corresponding implementations. We will see and understand various topics in graph machine learning such as different graph neural network architectures etc. (full list will be provided below!)

The notebooks are there to help you understand the material and teach you details of the PyTorch framework, Torch-Geometric framework and DGL framework, including PyTorch Lightning.

We encourage you to get familiar with the notebooks and experiment or extend them yourself. Further, the content presented will be relevant for the graded assignment and exam.

Schedule (Graph Machine Learning Foundation and Applications, edition 2024)

Date

Notebook

Tutorial 1: ..to be added

How to run the notebooks

On this website, you will find the notebooks exported into a HTML format so that you can read them from whatever device you prefer. However, we suggest that you also give them a try and run them yourself. There are three main ways of running the notebooks we recommend: - Locally on CPU: to be added - Locally on GPU: to be added - Google Colab: to be added

Tutorial-Lecture alignment

to be added soon

Feedback, Questions or Contributions

We present these tutorials as a self study for the Graph Machine Learning Foundation and Applications course. As with any other project, small bugs and issues are expected. We appreciate any feedback from students, whether it is about a spelling mistake, implementation bug, or suggestions for improvements/additions to the notebooks. Please use the following link to submit feedback, or feel free to reach out to me directly per mail (animesh dot sachan24794 at gmail dot com), or talk to me during any TA session.

If you find the tutorials helpful and would like to cite them, you can use the following bibtex:

@misc{animesh,
   title        = {{GMLFA:Graph Machine Learning Foundation and Applications, Tutorials}},
   author       = {Animesh},
   year         = 2024,
   howpublished = {\url{https://gmlfa-tutorials.readthedocs.io/en/latest/}}
}

Introduction:

NetworkX: