A Practical Guide to Graph Neural Networks

This repository contains the code for the extended examples in the paper "A Practical Guide to Graph Neural Networks".

If using the code here, or referencing the paper, please use the following bibtex citation entry for our preprint.

@misc{ward2020practical,
      title={A Practical Guide to Graph Neural Networks}, 
      author={Isaac Ronald Ward and Jack Joyner and Casey Lickfold and Stash Rowe and Yulan Guo and Mohammed Bennamoun},
      year={2020},
      eprint={2010.05234},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Folder structure

.
├── html            # Exported .html files of the notebooks
├── notebooks       # The .ipynb files of the example code
├── .gitignore                     
├── env.yml         # The conda environment dependencies file
├── LICENSE
└── README.md

Running on your own computer

Although Jupyter notebooks (notebooks/) and exported HTML files (html/) have been included in this repository for ease of viewing and sharing, you may still want to clone this repository and run / modify the code yourself.

To do this, use a conda-based package manager and install dependencies from the file env.yml .yml file. Do this using the following command (or similar):

conda env create -f env.yml

Activate this environment and run the jupyter notebook command.

This code has been tested using Python 3.8.3.