[NeurIPS 2021]-From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

Code for CCA-SSG model proposed in the NeurIPS 2021 paper From Canonical Correlation Analysis to Self-supervised Graph Neural Networks.

Dependencies

• Python 3.7
• PyTorch 1.7.1
• dgl 0.6.0

Datasets

Citation Networks: 'Cora', 'Citeseer' and 'Pubmed'.

Co-occurence Networks: 'Amazon-Computer', 'Amazon-Photo', 'Coauthor-CS' and 'Coauthor-Physics'.

Dataset	# Nodes	# Edges	# Classes	# Features
Cora	2,708	10,556	7	1,433
Citeseer	3,327	9,228	6	3,703
Pubmed	19,717	88,651	3	500
Amazon-Computer	13,752	574,418	10	767
Amazon-Photo	7,650	287,326	8	745
Coauthor-CS	18,333	327,576	15	6,805
Coauthor-Physics	34,493	991,848	5	8,451

Usage

To run the codes, use the following commands:

# Cora
python main.py --dataname cora --epochs 50 --lambd 1e-3 --dfr 0.1 --der 0.4 --lr2 1e-2 --wd2 1e-4

# Citeseer
python main.py --dataname citeseer --epochs 20 --n_layers 1 --lambd 5e-4 --dfr 0.0 --der 0.4 --lr2 1e-2 --wd2 1e-2

# Pubmed
python main.py --dataname pubmed --epochs 100 --lambd 1e-3 --dfr 0.3 --der 0.5 --lr2 1e-2 --wd2 1e-4

# Amazon-Computer
python main.py --dataname comp --epochs 50 --lambd 5e-4 --dfr 0.1 --der 0.3 --lr2 1e-2 --wd2 1e-4

# Amazon-Photo
python main.py --dataname photo --epochs 50 --lambd 1e-3 --dfr 0.2 --der 0.3 --lr2 1e-2 --wd2 1e-4

# Coauthor-CS
python main.py --dataname cs --epochs 50 --lambd 1e-3 --dfr 0.2 --lr2 5e-3 --wd2 1e-4 --use_mlp

# Coauthor-Physics
python main.py --dataname physics --epochs 100 --lambd 1e-3 --dfr 0.5 --der 0.5 --lr2 5e-3 --wd2 1e-4

Reference

If our paper and code are useful for your research, please cite the following article:

@inproceedings{zhang2021canonical,
  title={From canonical correlation analysis to self-supervised graph neural networks},
  author={Zhang, Hengrui and Wu, Qitian and Yan, Junchi and Wipf, David and Philip, S Yu},
  booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
  year={2021}
}