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A recent focal area in the space of graph neural networks (GNNs) is graph self-supervised learning (SSL), which aims to derive useful node representations without labeled data.
Relations between two sets of variates
Harold Hotelling · 1992
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Understanding negative sampling in graph representation learning, 2020
Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, and Jie Tang · 2005
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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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Link prediction on evolving data using matrix and tensor factorizations
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Line graph neural networks for link prediction, 2020
Lei Cai, Jundong Li, Jie Wang, and Shuiwang Ji · 2010
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Temporal link prediction using matrix and tensor factorizations
Daniel M Dunlavy, Tamara G Kolda, and Evrim Acar · 2011
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Link prediction via matrix factorization
Aditya Krishna Menon and Charles Elkan · 2011
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Deep canonical correlation analysis
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Graph convolutional matrix completion
Rianne van den Berg, Thomas N Kipf, and Max Welling · 2017
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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Similarity-based link prediction in social networks: A path and node combined approach
Chuanming Yu, Xiaoli Zhao, Lu An, and Xia Lin · 2017
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Pme: Projected metric embedding on heterogeneous networks for link prediction
Hongxu Chen, Hongzhi Yin, Weiqing Wang, Hao Wang, Quoc Viet Hung Nguyen, and Xue Li · 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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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Gunnemann · 2018
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Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2018
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Muhan Zhang and Yixin Chen · 2018
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Graph networks as a universal machine learning framework for molecules and crystals
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong · 2019
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Labeled graph generative adversarial networks
Shuangfei Fan and Bert Huang · 2019
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Muhan Zhang and Yixin Chen · 2019
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Few-shot graph learning for molecular property prediction
Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, and Nitesh V Chawla · 2021
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Automated self-supervised learning for graphs
Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, and Jiliang Tang · 2021
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Self-supervised graph neural networks without explicit negative sampling
Zekarias T. Kefato and Sarunas Girdzijauskas · 2021
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Graph neural networks for friend ranking in large-scale social platforms
Aravind Sankar, Yozen Liu, Jun Yu, and Neil Shah · 2021
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Adversarially generating rank-constrained graphs
William Shiao and Evangelos E Papalexakis · 2021
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A simple framework for contrastive learning of visual representations
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Bootstrap your own latent-a new approach to self-supervised learning
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Inductive link prediction for nodes having only attribute information
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Lightgcn: Simplifying and powering graph convolution network for recommendation
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Pairwise learning for neural link prediction
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