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Graph contrastive learning (GCL), as a popular approach to graph self-supervised learning, has recently achieved a non-negligible effect.
An application of the principle of maximum information preservation to linear systems
Ralph Linsker · 1988
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2017
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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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A theoretical analysis of contrastive unsupervised representation learning
Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, and Hrishikesh Khandeparkar · 2019
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Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang · 2019
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Deep graph infomax
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R. Devon Hjelm · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Bootstrap your own latent - A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
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Wiki-cs: A wikipedia-based benchmark for graph neural networks
Péter Mernyei and Cătălina Cangea · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Graph representation learning via graphical mutual information maximization
Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, and Junzhou Huang · 2020
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Deep graph contrastive representation learning
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2020
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Graph barlow twins: A self-supervised representation learning framework for graphs
Piotr Bielak, Tomasz Kajdanowicz, and Nitesh V. Chawla · 2021
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Augmentation-free self-supervised learning on graphs
Namkyeong Lee, Junseok Lee, and Chanyoung Park · 2021
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Mocl: Contrastive learning on molecular graphs with multi-level domain knowledge
Mengying Sun, Jing Xing, Huijun Wang, Bin Chen, and Jiayu Zhou · 2021
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Adversarial graph augmentation to improve graph contrastive learning
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville · 2021
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Large-scale representation learning on graphs via bootstrapping
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Veličković, and Michal Valko · 2021
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GCC: graph contrastive coding for graph neural network pre-training
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang · 2020
Cited alongside, same era.
Graph information bottleneck
Tailin Wu, Hongyu Ren, Pan Li, and Jure Leskovec · 2020
Cited alongside, same era.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
Cited alongside, same era.
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Self-supervised learning on graphs: Contrastive, generative,or predictive
Lirong Wu, Haitao Lin, Zhangyang Gao, Cheng Tan, and Stan. Z. Li · 2021
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Graph contrastive learning with adaptive augmentation
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2021
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