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Graph contrastive learning (GCL) is the most representative and prevalent self-supervised learning approach for graph-structured data.
The approximation of one matrix by another of lower rank
Carl Eckart and Gale Young · 1936
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Self-organization in a perceptual network
Ralph Linsker · 1988
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Birds of a feather: Homophily in social networks
Miller McPherson, Lynn Smith-Lovin, and James M Cook · 2001
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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Gaussian mixture models
Douglas A Reynolds · 2009
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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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Query-driven active surveying for collective classification
Galileo Namata, Ben London, Lise Getoor, Bert Huang, and UMD EDU · 2012
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Representation learning with contrastive predictive coding
Aaron Van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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A theoretical analysis of contrastive unsupervised representation learning
Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, and Nikunj Saunshi · 2019
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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Fast autoaugment
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, and Sungwoong Kim · 2019
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Revisiting graph neural networks: All we have is low-pass filters
Hoang Nt and Takanori Maehara · 2019
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On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, and George Tucker · 2019
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Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
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Transfer learning of graph neural networks with ego-graph information maximization
Qi Zhu, Yidan Xu, Haonan Wang, Chao Zhang, Jiawei Han, and Carl Yang · 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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Beyond low-frequency information in graph convolutional networks
Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen · 2021
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Not all low-pass filters are robust in graph convolutional networks
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Deep graph infomax
Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2019
Cited alongside, same era.
Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
Cited alongside, same era.
Analyzing the expressive power of graph neural networks in a spectral perspective
Muhammet Balcilar, Guillaume Renton, Pierre Héroux, Benoit Gaüzère, Sébastien Adam, and Paul Honeine · 2020
Cited alongside, same era.
All you need is low (rank) defending against adversarial attacks on graphs
Negin Entezari, Saba A Al-Sayouri, Amirali Darvishzadeh, and Evangelos E Papalexakis · 2020
Cited alongside, same era.
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Cited alongside, same era.
Contrastive multi-view representation learning on graphs
Kaveh Hassani and Amir Hosein Khasahmadi · 2020
Cited alongside, same era.
Faster autoaugment: Learning augmentation strategies using backpropagation
Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, and Hideki Nakayama · 2020
Cited alongside, same era.
Heng Chang, Yu Rong, Tingyang Xu, Yatao Bian, Shiji Zhou, Xin Wang, Junzhou Huang, and Wenwu Zhu · 2021
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Provable guarantees for self-supervised deep learning with spectral contrastive loss
Jeff Z HaoChen, Colin Wei, Adrien Gaidon, and Tengyu Ma · 2021
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A survey on contrastive self-supervised learning
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon · 2021
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Beyond low-pass filters: Adaptive feature propagation on graphs
Shouheng Li, Dongwoo Kim, and Qing Wang · 2021
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser Nam Lim · 2021
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New benchmarks for learning on non-homophilous graphs
Derek Lim, Xiuyu Li, Felix Hohne, and Ser-Nam Lim · 2021
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Is homophily a necessity for graph neural networks?
Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang · 2021
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Benedek Rozemberczki and Rik Sarkar · 2021
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Bootstrapped representation learning on graphs
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Veličković, and Michal Valko · 2021
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Contrastive learning, multi-view redundancy, and linear models
Christopher Tosh, Akshay Krishnamurthy, and Daniel Hsu · 2021
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Self-supervised learning of graph neural networks: A unified review
Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhengyang Wang, and Shuiwang Ji · 2021
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An empirical study of graph contrastive learning
Yanqiao Zhu, Yichen Xu, Qiang Liu, and Shu Wu · 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
Later among the works it cites.
Augmentations in graph contrastive learning: Current methodological flaws & towards better practices
Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan, Yaoqing Yang, and Danai Koutra · 2022
Closest in time.
Fastgcl: Fast self-supervised learning on graphs via contrastive neighborhood aggregation
Yuansheng Wang, Wangbin Sun, Kun Xu, Zulun Zhu, Liang Chen, and Zibin Zheng · 2022
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