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This work develops \emph{mixup for graph data}.
Augmenting data with mixup for sentence classification: An empirical study
Guo, H., Mao, Y., and Zhang, R · 1905
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Graphmix: Improved training of gnns for semi-supervised learning
Verma, V., Qu, M., Kawaguchi, K., Lamb, A., Bengio, Y., Kannala, J., and Tang, J · 1909
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Quick approximation to matrices and applications
Frieze, A. and Kannan, R · 1999
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Limits of dense graph sequences
Lovász, L. and Szegedy, B · 2006
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Convergent sequences of dense graphs i: Subgraph frequencies, metric properties and testing
Borgs, C., Chayes, J. T., Lovász, L., Sós, V. T., and Vesztergombi, K · 2008
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Graphcrop: Subgraph cropping for graph classification
Wang, Y., Wang, W., Liang, Y., Cai, Y., and Hooi, B · 2009
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Matrix completion from a few entries
Keshavan, R. H., Montanari, A., and Oh, S · 2010
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Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E · 2010
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Graph and graphon neural network stability
Ruiz, L., Wang, Z., and Ribeiro, A · 2010
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Classification and estimation in the stochastic blockmodel based on the empirical degrees
Channarond, A., Daudin, J.-J., and Robin, S · 2012
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Large networks and graph limits , volume 60
Lovász, L · 2012
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Stochastic blockmodel approximation of a graphon: Theory and consistent estimation
Airoldi, E. M., Costa, T. B., and Chan, S. H · 2013
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A consistent histogram estimator for exchangeable graph models
Chan, S. and Airoldi, E · 2014
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Matrix estimation by universal singular value thresholding
Chatterjee, S. et al · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
Cited alongside, same era.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2017
Cited alongside, same era.
Centrality measures for graphons: Accounting for uncertainty in networks
Avella-Medina, M., Parise, F., Schaub, M. T., and Segarra, S · 2018
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2020
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Dropedge: Towards deep graph convolutional networks on node classification
Rong, Y., Huang, W., Xu, T., and Huang, J · 2020
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A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Philip, S. Y · 2020
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Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y · 2020
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Deep learning on graphs: A survey
Zhang, Z., Cui, P., and Zhu, W · 2020
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Training graph neural networks by graphon estimation, 2021
Hu, Z., Fang, Y., and Lin, L · 2021
Later among the works it cites.
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Huang, W., Zhang, T., Rong, Y., and Huang, J · 2018
Cited alongside, same era.
How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2018
Cited alongside, same era.
Hierarchical graph representation learning with differentiable pooling
Ying, R., You, J., Morris, C., Ren, X., Hamilton, W. L., and Leskovec, J · 2018
Cited alongside, same era.
An end-to-end deep learning architecture for graph classification
Zhang, M., Cui, Z., Neumann, M., and Chen, Y · 2018
Cited alongside, same era.
How robust are graph neural networks to structural noise?
Fox, J. and Rajamanickam, S · 2019
Cited alongside, same era.
Gao, H. and Ji, S · 2019
Cited alongside, same era.
Graph theory and additive combinatorics, 2019
Zhao, Y · 2019
Cited alongside, same era.
Metropolis-hastings data augmentation for graph neural networks
Park, H., Lee, S., Kim, S., Park, J., Jeong, J., Kim, K.-M., Ha, J.-W., and Kim, H. J · 2021
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Adversarial graph augmentation to improve graph contrastive learning
Suresh, S., Li, P., Hao, C., and Neville, J · 2021
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Data augmentation for graph convolutional network on semi-supervised classification
Tang, Z., Qiao, Z., Hong, X., Wang, Y., Dharejo, F. A., Zhou, Y., and Du, Y · 2021
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The laplacian spectrum of large graphs sampled from graphons
Vizuete, R., Garin, F., and Frasca, P · 2021
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Mixup for node and graph classification
Wang, Y., Wang, W., Liang, Y., Cai, Y., and Hooi, B · 2021
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Learning graphons via structured gromov-wasserstein barycenters
Xu, H., Luo, D., Carin, L., and Zha, H · 2021
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How does mixup help with robustness and generalization?
Zhang, L., Deng, Z., Kawaguchi, K., Ghorbani, A., and Zou, J · 2021
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Data augmentation for graph neural networks
Zhao, T., Liu, Y., Neves, L., Woodford, O., Jiang, M., and Shah, N · 2021
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Bringing your own view: Graph contrastive learning without prefabricated data augmentations
You, Y., Chen, T., Wang, Z., and Shen, Y · 2022
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