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[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}
}