Fairness for Machine Learning on Graphs : a Survey

This repository contains additional elements for the survey paper on fairness for Machine Learning, including :

• benchmarks.md provide links to download the benchmark graphs listed in the article
• sota.md contains links to the github repositories with state-of-the-art models implementation.
• datais a folder that contains links to the benchmark datasets mentioned in the survey.
• synthetic is a folder that contains the code to generate the synthetic graphs G1--G6 and the script to produce the visualisation presented in the survey. Three files can be found in this folder:
  • generate_graph.py contains the required functions to generate the 6 use-cases presented in the survey
  • script_visualisation.py allows to generate and visualise the graphs
  • save_graph.py allows to generate the graph and save it

Example to visualise and save g1

  python script_visualisation.py g1
  python save_graph.py g1 graphs

If your paper does not appear in this survey, but seems relevant to its contents, please let us know, and we will try to include it in the revised versions.

Citation

The paper can be found [[http://arxiv.org/abs/2205.05396]] (arxiv link).

If you plan on using some of our ressources or the paper itself, please cite our work as follows

@article{choudhary2022,
title = {A Survey on Fairness for Machine Learning on Graphs},
authors = {Choudhary, Manvi and Laclau, Charlotte and Largeron, Christine},
journal = {CoRR},
year = {2022}
}