Flexible Fairness Constraints for Graph Embeddings

Setup

Dependencies

1. Comet ML for logging. You will need an API key, username, and project name to do online logging.
2. Pytorch version=1.0
3. scikit-learn
4. tqdm for progress bar
5. pickle
6. json
7. joblib
8. networkx for creating reddit graph

Datasets

• MovieLens-1M (used by the paper)

Inside the repository folder :

# Download Dataset
git clone https://github.com/cmoon2/knowledge_graph.git
wget https://download.microsoft.com/download/8/7/0/8700516A-AB3D-4850-B4BB-805C515AECE1/FB15K-237.2.zip
# Unzip Datasets
unzip -qq ./FB15K-237.2.zip

• Freebase15k - 237 (not tested in the paper)

Inside the repository folder :

# Download Dataset
wget --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-1m.zip
# Unzip Datasets
unzip -qq ./ml-1m.zip
# Renaming to match path specified in code
mv Release fb15k

Commands

To reproduce the results we provide sample commands. Command Line arguments control which sensitive attributes are use and whether there is a compositional adversary or not.

1. MovieLens1M:

ipython --pdb -- main_movielens.py --namestr='100 GCMC Comp and Dummy' --use_cross_entropy --num_epochs=200 --test_new_disc --use_1M=True --show_tqdm=True --report_bias=True --valid_freq=5 --use_gcmc=True --num_classifier_epochs=200 --embed_dim=30 --sample_mask=True --use_attr=True --gamma=10 --do_log

2. FB15k-237:

python construct_ent_attributes.py --dataset FB15k

python parse.py --dataset FB15k

ipython --pdb -- paper_trans_e.py --namestr='FB15k Comp Gamma=1000' --do_log --num_epochs=100 --embed_dim=20 --test_new_disc --sample_mask=True --use_attr=True --gamma=1000 --valid_freq=50

Paper Link: https://arxiv.org/abs/1905.10674