Code and data for our demo 'Efficient Fair Graph Representation Learning Using a Multi-level Framework' in WWW '23.
Our codes are implemented in Python 3.8. All required packages are included in requirements.yml.
We have seven real-world datasets in our experiments. Namely, they are:
- Node classification: German, Bail (Recidivism), Credit, and Pokec-n
- Link prediction: Cora, Citeseer, Pubmed
This repository only includes pre-processed data for node classification datasets (except Pokec-n). You can download raw data for all datasets (and pre-processed Pokec-n) at this link.
Pre-processed data for node classification datasets can be rebuilt using preprocess.py. For link prediction datasets, it will automatically process the raw data in datasets/raw/{dataset} and store the processed version at the time of first use. Processed link prediction datasets are stored in datasets/{dataset}/{seed}.
For instance, to run FairMILE with NetMF on german (c=2), please use this command:
python main.py --data german --basic-embed netmf --coarsen-level 2
To run the vanilla NetMF, simply add an argument --baseline:
python main.py --data german --basic-embed netmf --baseline
To run FairMILE or baselines in link prediction tasks, please add an argument --task lp:
python main.py --task lp --data cora --basic-embed node2vec --coarsen-level 2
python main.py --task lp --data cora --basic-embed node2vec --baseline
Please check main.py for a comprehensive list of arguments.
If you have any questions, feel free to contact me.