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Graph Neural Network (GNN) is an emerging technique for graph-based learning tasks such as node classification.
Ting, Kai Ming, A comparative study of cost-sensitive boosting algorithms
2000
Earlier work this paper cites.
Japkowicz, Nathalie, and Shaju Stephen, The class imbalance problem: A systematic study
2002
Earlier work this paper cites.
Chawla, Nitesh V., et al, SMOTE: synthetic minority over-sampling technique
2002
Earlier work this paper cites.
Drummond, Chris, and Robert C. Holte, C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling
2003
Earlier work this paper cites.
Weiss, Gary M, Mining with rarity: a unifying framework
2004
Earlier work this paper cites.
G. M. Weiss, Mining with rarity: a unifying framework
2004
Earlier work this paper cites.
Han, Hui, Wen-Yuan Wang, and Bing-Huan Mao. ”Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning.” International conference on intelligent computing
2005
Earlier work this paper cites.
Liu, Xu-Ying, Jianxin Wu, and Zhi-Hua Zhou. ”Exploratory undersampling for class-imbalance learning.” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
2008
Cited alongside, same era.
P. Sen, Galileo Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad. Collective Classification in Network Data. AI Magazine
2008
Cited alongside, same era.
Scott, John and Carrington, Peter J, ”The SAGE handbook of social network analysis.” SAGE publications
2011
Cited alongside, same era.
Bruna, Joan, et al. ”Spectral networks and locally connected networks on graphs.” arXiv preprint
2013
Cited alongside, same era.
2016
Hamilton, Will, Zhitao Ying, and Jure Leskovec, Inductive representation learning on large graphs
2017
Later among the works it cites.
Velikovi, Petar, et al. ”Graph attention networks.” arXiv preprint
2017
Later among the works it cites.
Dong, Qi, Shaogang Gong, and Xiatian Zhu. ”Imbalanced deep learning by minority class incremental rectification.” IEEE transactions on pattern analysis and machine intelligence
2018
Later among the works it cites.
Hou, Yifan, et al. ”Measuring and improving the use of graph information in graph neural networks.” International Conference on Learning Representations
2019
Later among the works it cites.
Wang, Yiru, et al. ”Dynamic curriculum learning for imbalanced data classification.” Proceedings of the IEEE/CVF International Conference on Computer Vision
2019
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Cited alongside, same era.
Yu, Hualong, et al. ”ODOC-ELM: Optimal decision outputs compensation-based extreme learning machine for classifying imbalanced data.” Knowledge-Based Systems
2016
Cited alongside, same era.
Defferrard, Michaël, Xavier Bresson, and Pierre Vandergheynst. ”Convolutional neural networks on graphs with fast localized spectral filtering.” Advances in neural information processing systems
2016
Cited alongside, same era.
Later among the works it cites.
Chen, Deli, et al. ”Measuring and relieving the over-smoothing problem for graph neural networks from the topological view.” Proceedings of the AAAI Conference on Artificial Intelligence
2020
Later among the works it cites.
Zhao, Tianxiang, Xiang Zhang, and Suhang Wang. ”GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks.” Proceedings of the 14th ACM International Conference on Web Search and Data Mining
2021
Later among the works it cites.