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We present the SCR framework for enhancing the training of graph neural networks (GNNs) with consistency regularization.
Learning with local and global consistency
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Semi-supervised learning using gaussian fields and harmonic functions
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Y. Grandvalet and Y. Bengio · 2004
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Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
M. Belkin, P. Niyogi, and V. Sindhwani · 2006
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Semi-supervised learning
O. Chapelle, B. Schölkopf, and A. Zien · 2006
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A general optimization framework for smoothing language models on graph structures
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Learning with pseudo-ensembles
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Dropout: a simple way to prevent neural networks from overfitting
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The extreme classification repository: Multi-label datasets and code, 2016
K. Bhatia, K. Dahiya, H. Jain, P. Kar, A. Mittal, Y. Prabhu, and M. Varma · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
M. Sajjadi, M. Javanmardi, and T. Tasdizen · 2016
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Inductive representation learning on large graphs
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
A. Tarvainen and H. Valpola · 2017
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Fastgcn: fast learning with graph convolutional networks via importance sampling
J. Chen, T. Ma, and C. Xiao · 2018
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Adaptive sampling towards fast graph representation learning
W. Huang, T. Zhang, Y. Rong, and J. Huang · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Q. Li, Z. Han, and X.-M. Wu · 2018
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Modeling relational data with graph convolutional networks
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. v. d. Berg, I. Titov, and M. Welling · 2018
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Graph attention networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2018
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mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2018
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Mixmatch: A holistic approach to semi-supervised learning
D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel · 2019
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
W. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, and C. Hsieh · 2019
Lightgcn: Simplifying and powering graph convolution network for recommendation
X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang · 2020
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Open graph benchmark: Datasets for machine learning on graphs
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec · 2020
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Towards deeper graph neural networks
M. Liu, H. Gao, and S. Ji · 2020
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Dropedge: Towards deep graph convolutional networks on node classification
Y. Rong, W. Huang, T. Xu, and J. Huang · 2020
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Extreme multi-label classification from aggregated labels
Y. Shen, H.-f. Yu, S. Sanghavi, and I. Dhillon · 2020
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Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes
K. Sun, Z. Lin, and Z. Zhu · 2020
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Batch virtual adversarial training for graph convolutional networks
Z. Deng, Y. Dong, and J. Zhu · 2019
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Graph adversarial training: Dynamically regularizing based on graph structure
F. Feng, X. He, J. Tang, and T.-S. Chua · 2019
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Invariant and equivariant graph networks
H. Maron, H. Ben-Hamu, N. Shamir, and Y. Lipman · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al · 2019
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Simplifying graph convolutional networks
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger · 2019
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How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
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Unsupervised data augmentation for consistency training
Q. Xie, Z. Dai, E. Hovy, M.-T. Luong, and Q. V. Le · 2020
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Graphsaint: Graph sampling based inductive learning method
H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. Prasanna · 2020
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Cogdl: An extensive toolkit for deep learning on graphs
Y. Cen, Z. Hou, Y. Wang, Q. Chen, Y. Luo, X. Yao, A. Zeng, S. Guo, P. Zhang, G. Dai, Y. Wang, C. Zhou, H. Yang, and J. Tang · 2021
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Node feature extraction by self-supervised multi-scale neighborhood prediction
E. Chien, W.-C. Chang, C.-J. Hsieh, H.-F. Yu, J. Zhang, O. Milenkovic, and I. S. Dhillon · 2021
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Combining label propagation and simple models out-performs graph neural networks
Q. Huang, H. He, A. Singh, S.-N. Lim, and A. Benson · 2021
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R-drop: Regularized dropout for neural networks
X. Liang, L. Wu, J. Li, Y. Wang, Q. Meng, T. Qin, W. Chen, M. Zhang, and T. Liu · 2021
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Scalable and adaptive graph neural networks with self-label-enhanced training
C. Sun and G. Wu · 2021
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Graphmix: Improved training of gnns for semi-supervised learning
V. Verma, M. Qu, K. Kawaguchi, A. Lamb, Y. Bengio, J. Kannala, and J. Tang · 2021
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Graph attention multi-layer perceptron
W. Zhang, Z. Yin, Z. Sheng, W. Ouyang, X. Li, Y. Tao, Z. Yang, and B. Cui · 2021
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