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Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many domains.
Learning from labeled and unlabeled data with label propagation
X. Zhu and Z. Ghahramani · 2002
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Semi-supervised learning using gaussian fields and harmonic functions
X. Zhu, Z. Ghahramani, and J. D. Lafferty · 2003
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Coauthorship networks and patterns of scientific collaboration
M. E. Newman · 2004
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Learning with local and global consistency
D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf · 2004
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Semi-supervised learning literature survey
X. J. Zhu · 2005
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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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Hyperparameter learning for graph based semi-supervised learning algorithms
X. Zhang, W. S. Lee, et al · 2006
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Learning on graph with laplacian regularization
R. K. Ando and T. Zhang · 2007
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Curriculum learning
Y. Bengio, J. Louradour, R. Collobert, and J. Weston · 2009
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Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2013
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Exploiting homophily effect for trust prediction
J. Tang, H. Gao, X. Hu, and H. Liu · 2013
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Deepwalk: Online learning of social representations
B. Perozzi, R. Al-Rfou, and S. Skiena · 2014
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One trillion edges: Graph processing at facebook-scale
A. Ching, S. Edunov, M. Kabiljo, D. Logothetis, and S. Muthukrishnan · 2015
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Self-paced curriculum learning
L. Jiang, D. Meng, Q. Zhao, S. Shan, and A. Hauptmann · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Disturblabel: Regularizing cnn on the loss layer
L. Xie, J. Wang, Z. Wei, M. Wang, and Q. Tian · 2016
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Stochastic training of graph convolutional networks with variance reduction
J. Chen, J. Zhu, and L. Song · 2017
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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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Towards robust detection of adversarial examples
T. Pang, C. Du, Y. Dong, and J. Zhu · 2017
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Regularizing neural networks by penalizing confident output distributions
G. Pereyra, G. Tucker, J. Chorowski, Ł. Kaiser, and G. Hinton · 2017
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P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 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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Large-scale learnable graph convolutional networks
H. Gao, Z. Wang, and S. Ji · 2018
Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach
L. Chen, L. Wu, R. Hong, K. Zhang, and M. Wang · 2020
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Simple and deep graph convolutional networks
M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li · 2020
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Towards a better understanding of label smoothing in neural machine translation
Y. Gao, W. Wang, C. Herold, Z. Yang, and H. Ney · 2020
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Learning better structured representations using low-rank adaptive label smoothing
A. Ghoshal, X. Chen, S. Gupta, L. Zettlemoyer, and Y. Mehdad · 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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Billion-scale commodity embedding for e-commerce recommendation in alibaba
J. Wang, P. Huang, H. Zhao, Z. Zhang, B. Zhao, and D. L. Lee · 2018
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Acekg: A large-scale knowledge graph for academic data mining
R. Wang, Y. Yan, J. Wang, Y. Jia, Y. Zhang, W. Zhang, and X. Wang · 2018
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How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2018
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Graph convolutional neural networks for web-scale recommender systems
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec · 2018
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Hierarchical graph representation learning with differentiable pooling
R. Ying, J. You, C. Morris, X. Ren, W. L. Hamilton, and J. Leskovec · 2018
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
W.-L. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, and C.-J. Hsieh · 2019
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Adaptive regularization of labels
Q. Ding, S. Wu, H. Sun, J. Guo, and S.-T. Xia · 2019
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Q. Huang, H. He, A. Singh, S.-N. Lim, and A. R. Benson · 2020
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Residual correlation in graph neural network regression
J. Jia and A. R. Benson · 2020
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Regularization via structural label smoothing
W. Li, G. Dasarathy, and V. Berisha · 2020
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Generalized entropy regularization or: There’s nothing special about label smoothing
C. Meister, E. Salesky, and R. Cotterell · 2020
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Sign: Scalable inception graph neural networks
E. Rossi, F. Frasca, B. Chamberlain, D. Eynard, M. Bronstein, and F. Monti · 2020
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Masked label prediction: Unified message passing model for semi-supervised classification
Y. Shi, Z. Huang, W. Wang, H. Zhong, S. Feng, and Y. Sun · 2020
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Unifying graph convolutional neural networks and label propagation
H. Wang and J. Leskovec · 2020
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On the inference calibration of neural machine translation
S. Wang, Z. Tu, S. Shi, and Y. Liu · 2020
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Towards understanding label smoothing
Y. Xu, Y. Xu, Q. Qian, H. Li, and R. Jin · 2020
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Revisiting knowledge distillation via label smoothing regularization
L. Yuan, F. E. Tay, G. Li, T. Wang, and J. Feng · 2020
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Towards deeper graph neural networks with differentiable group normalization
K. Zhou, X. Huang, Y. Li, D. Zha, R. Chen, and X. Hu · 2020
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From label smoothing to label relaxation
J. Lienen and E. Hüllermeier · 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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Temporal augmented graph neural networks for session-based recommendations
H. Zhou, Q. Tan, X. Huang, K. Zhou, and X. Wang · 2021
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Dirichlet energy constrained learning for deep graph neural networks
K. Zhou, X. Huang, D. Zha, R. Chen, L. Li, S.-H. Choi, and X. Hu · 2021
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