Source code for NeurIPS 2022 paper "Uncovering the Structural Fairness in Graph Contrastive Learning"
- python == 3.7
- torch == 1.11.0, cuda == 11.3
- Deep Graph Library == 0.8.0
- numpy == 1.21.2
- torch_scatter == 2.0.9
- networkx == 2.6.3
- scikit-learn == 1.0.2
-
model
- threshold: the threshold to divide tail and head nodes
- temp: the temperature for similarity
- der1: the drop edge ratio of the 1st augmentation
- der2: the drop edge ratio of the 2nd augmentation
- dfr1: the drop feature ratio of the 1st augmentation
- dfr2: the drop feature ratio of the 2nd augmentation
-
trainer
- mode: train-test split setting (full/part)
- warmup: the warmup epochs of training
GRADE/
├── code/
│ ├── main.py: training the GRADE model
│ ├── aug.py: the implementation of the proposed degree-aware augmentation
│ ├── model.py
│ └── utils.py
├── data/
└── README.md
To replicate GRADE results from Table 1 and Table 2, run
# Cora dataset
python main.py --dataset cora --mode full --hid_dim 256 --out_dim 256 --act_fn relu --temp 0.5 --save_name best_cora.pkl --test
# Citeseer dataset
python main.py --dataset citeseer --mode full --hid_dim 256 --out_dim 256 --act_fn relu --temp 1.7 --save_name best_citeseer.pkl --test
# Photo dataset
python main.py --dataset photo --mode full --hid_dim 512 --out_dim 512 --act_fn relu --temp 0.8 --save_name best_photo.pkl --test
# Computer dataset
python main.py --dataset computer --mode full --hid_dim 800 --out_dim 800 --act_fn prelu --temp 1.1 --save_name best_computer.pkl --test
@inproceedings{wang2022uncovering,
title={Uncovering the Structural Fairness in Graph Contrastive Learning},
author={Wang, Ruijia and Wang, Xiao and Shi, Chuan and Song, Le},
booktitle={Proceedings of 36th Conference on Neural Information Processing Systems},
year={2022}
}