FairGen

Code for the paper: FairGen: Towards Fair Graph Generation

Introduction

FairGen is an end-to-end deep generative model that can directly learn from the raw graph while preserving the fair graph structure of both the majority and minority groups. It jointly trains a label-informed graph generation module and a fair representation learning module by progressively learning the behaviors of the protected and unprotected groups, from the 'easy' concepts to the 'hard' ones. In addition, it incorporates a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability.

Requirement:

• Scipy < 1.13 (tested on 1.12)

Tested Environment:

• Python: 3.12
• Pytorch: 2.3
• Cuda: 12.1
• Scipy: 1.12
• Scikit-learn: 4.3.2
• Gensim: 1.4.2

Environment and Installation:

1. conda env create -f environment.yml
2. conda activate fairgen

Command

1. Unzip the dataset:

unzip data.zip

2. Training and evaluation:

python main.py -d FLICKR -b

Some important flags:

• -d: the name of the dataset
• -g: the index of the gpu, 0 is the default value. If not using gpu, ignore this flag.
• -b: the biased random walk or unbiased random walk. Biased random walk depends on the node proximity, while unbiased random walk is independent of node proximity. The default value is a biased random walk with this flag.

Evaluation：

The edge list of the synthetic graph is stored in the directory: "'./data/FLICKR/FLICKR_output_edgelist_0_2.txt".

The final results will be stored in the directory: "./data/FLICKR/FLICKR_output_edgelist_0_2_metric.txt".

Please cite our paper if you find it useful:

@article{zheng2023fairgen,

title={Fairgen: Towards fair graph generation},

author={Zheng, Lecheng and Zhou, Dawei and Tong, Hanghang and Xu, Jiejun and Zhu, Yada and He, Jingrui},

journal={arXiv preprint arXiv:2303.17743},

year={2023} }