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Recent years have witnessed great success in handling node classification tasks with Graph Neural Networks (GNNs).
Markov decision processes
White III, C. C.; and White, D. J. 1989 · 1989
Earlier work this paper cites.
Q-learning
Watkins, C. J.; and Dayan, P. 1992 · 1992
Earlier work this paper cites.
A fast and high quality multilevel scheme for partitioning irregular graphs
Karypis, G.; and Kumar, V. 1998 · 1998
Earlier work this paper cites.
SMOTE: synthetic minority over-sampling technique
Chawla, N. V.; Bowyer, K. W.; Hall, L. O.; and Kegelmeyer, W. P. 2002 · 2002
Earlier work this paper cites.
Self-supervised graph representation learning via global context prediction
Peng, Z.; Dong, Y.; Luo, M.; Wu, X.-M.; and Zheng, Q. 2020 · 2003
Earlier work this paper cites.
Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning
Han, H.; Wang, W.-Y.; and Mao, B.-H. 2005 · 2005
Earlier work this paper cites.
Training cost-sensitive neural networks with methods addressing the class imbalance problem
Zhou, Z.-H.; and Liu, X.-Y. 2005 · 2005
Earlier work this paper cites.
Self-supervised learning on graphs: Deep insights and new direction
Jin, W.; Derr, T.; Liu, H.; Wang, Y.; Wang, S.; Liu, Z.; and Tang, J. 2020 · 2006
Earlier work this paper cites.
Wiki-cs: A wikipedia-based benchmark for graph neural networks
Mernyei, P.; and Cangea, C. 2020 · 2007
Earlier work this paper cites.
Collective classification in network data
Sen, P.; Namata, G.; Bilgic, M.; Getoor, L.; Galligher, B.; and Eliassi-Rad, T. 2008 · 2008
Earlier work this paper cites.
Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem
Bunkhumpornpat, C.; Sinapiromsaran, K.; and Lursinsap, C. 2009 · 2009
Cited alongside, same era.
Relational learning via latent social dimensions
Tang, L.; and Liu, H. 2009 · 2009
Cited alongside, same era.
Cost-sensitive learning and the class imbalance problem
Ling, C. X.; and Sheng, V. S. 2008 · 2011
Cited alongside, same era.
Sampling+ reweighting: Boosting the performance of AdaBoost on imbalanced datasets
Yuan, B.; and Ma, X. 2012 · 2012
Cited alongside, same era.
Optimizing F-measures by cost-sensitive classification
Parambath, S. P.; Usunier, N.; and Grandvalet, Y. 2014 · 2014
Cited alongside, same era.
Survey of resampling techniques for improving classification performance in unbalanced datasets
mixup: Beyond empirical risk minimization
Zhang, H.; Cisse, M.; Dauphin, Y. N.; and Lopez-Paz, D. 2017 · 2017
Later among the works it cites.
Handling imbalanced data: a survey
Rout, N.; Mishra, D.; and Mallick, M. K. 2018 · 2018
Later among the works it cites.
Survey on deep learning with class imbalance
Johnson, J. M.; and Khoshgoftaar, T. M. 2019 · 2019
Later among the works it cites.
Disentangled graph convolutional networks
Ma, J.; Cui, P.; Kuang, K.; Wang, X.; and Zhu, W. 2019 · 2019
Later among the works it cites.
Manifold mixup: Better representations by interpolating hidden states
Verma, V.; Lamb, A.; Beckham, C.; Najafi, A.; Mitliagkas, I.; Lopez-Paz, D.; and Bengio, Y. 2019 · 2019
Later among the works it cites.
Independence Promoted Graph Disentangled Networks
Liu, Y.; Wang, X.; Wu, S.; and Xiao, Z. 2020 · 2020
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More, A. 2016 · 2016
Cited alongside, same era.
Deep over-sampling framework for classifying imbalanced data
Ando, S.; and Huang, C. Y. 2017 · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W.; Ying, Z.; and Leskovec, J. 2017 · 2017
Cited alongside, same era.
Veličković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; and Bengio, Y. 2017 · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2016a
Cited in the paper.
Variational graph auto-encoders
Kipf, T. N.; and Welling, M. 2016b
Cited in the paper.
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Factorizable Graph Convolutional Networks
Yang, Y.; Feng, Z.; Song, M.; and Wang, X. 2020 · 2020
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Self-supervised on Graphs: Contrastive, Generative, or Predictive
Wu, L.; Lin, H.; Gao, Z.; Tan, C.; Li, S.; et al. 2021 · 2021
Closest in time.
GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks
Zhao, T.; Zhang, X.; and Wang, S. 2021 · 2021
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