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The complexity and non-Euclidean structure of graph data hinder the development of data augmentation methods similar to those in computer vision.
1911
Earlier work this paper cites.
F. Scarselli, C. A. Tsoi, M. Gori, M. Hagenbuchner, Graphical-based learning environments for pattern recognition, SSPR/SPR (2004) 42–56
2004
Earlier work this paper cites.
2008
Earlier work this paper cites.
B. Perozzi, R. Al-Rfou’, S. Skiena, Deepwalk: online learning of social representations, KDD (2014) 701–710
2014
Earlier work this paper cites.
A. Grover, J. Leskovec, node2vec: Scalable feature learning for networks, KDD (2016) 855–864
2016
Earlier work this paper cites.
A. Fout, J. Byrd, B. Shariat, A. Ben-Hur, Protein interface prediction using graph convolutional networks, ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017) (2017) 6533–6542
2017
Earlier work this paper cites.
X. Qi, R. Liao, J. Jia, S. Fidler, R. Urtasun, 3d graph neural networks for rgbd semantic segmentation, ICCV (2017) 5209–5218
2017
Earlier work this paper cites.
L. W. Hamilton, R. Ying, J. Leskovec, Inductive representation learning on large graphs, ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017) (2017) 1024–1034
2017
Cited alongside, same era.
J. Qiu, J. Tang, H. Ma, Y. Dong, K. Wang, J. Tang, Deepinf: Social influence prediction with deep learning, KDD (2018) 2110–2119
2018
Cited alongside, same era.
Q. Li, Z. Han, X.-M. Wu, Deeper insights into graph convolutional networks for semi-supervised learning, AAAI (2018) 3538–3545
2018
Cited alongside, same era.
Y. Wang, Y. Sun, Z. Liu, E. S. Sarma, M. M. Bronstein, M. J. Solomon, Dynamic graph cnn for learning on point clouds, ACM Transactions on Graphics (2019) 1–12
2019
Cited alongside, same era.
M. Tu, G. Wang, J. Huang, Y. Tang, X. He, B. Zhou, Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs, ACL (1) (2019) 2704–2713
D. E. Cubuk, B. Zoph, D. Mane, V. Vasudevan, V. Q. Le, Autoaugment - learning augmentation strategies from data, CVPR (2019) 113–123
2019
Later among the works it cites.
M. Chen, Z. Wei, Z. Huang, B. Ding, Y. Li, Simple and deep graph convolutional networks, ICML (2020) 1725–1735
2020
Later among the works it cites.
Q. Xie, Z. Dai, E. Hovy, M.-T. Luong, V. Q. Le, Unsupervised data augmentation for consistency training, NIPS 2020
2020
Later among the works it cites.
M. Liu, H. Gao, S. Ji, Towards deeper graph neural networks, KDD ’20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event CA USA July, 2020 (2020) 338–348
2020
Later among the works it cites.
Y. Wu, Y. Song, H. Huang, F. Ye, X. Xie, H. Jin, Enhancing graph neural networks via auxiliary training for semi-supervised node classification, Knowledge Based Systems 220 (2021) 106884
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2019
Cited alongside, same era.
C. Li, D. Goldwasser, Encoding social information with graph convolutional networks forpolitical perspective detection in news media, ACL (1) (2019) 2594–2604
2019
Cited alongside, same era.
A. Zhao, G. Balakrishnan, F. Durand, V. J. Guttag, V. A. Dalca, Data augmentation using learned transforms for one-shot medical image segmentation, CVPR (2019) 8543–8553
2019
Cited alongside, same era.
K. Do, T. Tran, S. Venkatesh, Graph transformation policy network for chemical reaction prediction, knowledge discovery and data mining
Cited in the paper.
Y. Zhang, X. Chen, Y. Yang, A. Ramamurthy, B. Li, Y. Qi, L. Song, Efficient probabilistic logic reasoning with graph neural networks, ICLR
Cited in the paper.
X. Xu, W. Feng, Y. Jiang, X. Xie, Z. Sun, Z.-H. Deng, Dynamically pruned message passing networks for large-scale knowledge graph reasoning, ICLR
Cited in the paper.
L. Yao, C. Mao, Y. Luo, Graph convolutional networks for text classification, national conference on artificial intelligence
Cited in the paper.
Z. Tong, L. Yozen, N. Leonardo, W. Oliver, J. Meng, S. Neil, Data augmentation for graph neural networks, AAAI
Cited in the paper.
2021
Closest in time.
K. Sun, Z. Lin, Z. Zhu, Adagcn: Adaboosting graph convolutional networks into deep models, ICLR 2021
2021
Closest in time.