[29] Michael R Smith, Tony Martinez, and Christophe Giraud-Carrier. An instance level analysis of data complexity. Machine learning, 95(2):225-256, 2014.
[30] Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. Advances in neural information processing systems, 33:596-608, 2020.
[31] Jaeyun Song, Joonhyung Park, and Eunho Yang. Tam: Topology-aware margin loss for class-imbalanced node classification. In International Conference on Machine Learning, pages 20369-20383. PMLR, 2022.
[32] Kaihua Tang, Jianqiang Huang, and Hanwang Zhang. Long-tailed classification by keeping the good and removing the bad momentum causal effect. Advances in Neural Information Processing Systems, 33:1513-1524, 2020.
[33] Yuchun Tang, Yan-Qing Zhang, Nitesh V Chawla, and Sven Krasser. Svms modeling for highly imbalanced classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(1):281-288, 2008.
[34] Junjiao Tian, Yen-Cheng Liu, Nathaniel Glaser, Yen-Chang Hsu, and Zsolt Kira. Posterior re-calibration for imbalanced datasets. Advances in Neural Information Processing Systems, 33:8101-8113, 2020.
[35] Petar Velicković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. arXiv preprint arXiv:1710.10903, 2017.
[36] Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. Deep graph infomax. 2018.
[37] Jianfeng Wang, Thomas Lukasiewicz, Xiaolin Hu, Jianfei Cai, and Zhenghua Xu. Rsg: A simple but effective module for learning imbalanced datasets. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3784-3793, 2021.
[38] Tong Wang, Yousong Zhu, Chaoyang Zhao, Wei Zeng, Jinqiao Wang, and Ming Tang. Adaptive class suppression loss for long-tail object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3103-3112, 2021.
[39] Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella X Yu. Long-tailed recognition by routing diverse distribution-aware experts. arXiv preprint arXiv:2010.01809, 2020.
[40] Zheng Wang, Xiaojun Ye, Chaokun Wang, Jian Cui, and S Yu Philip. Network embedding with completely-imbalanced labels. IEEE Transactions on Knowledge and Data Engineering, 33(11):3634-3647, 2020.
[41] Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. Unsupervised data augmentation for consistency training. Advances in neural information processing systems, 33:6256-6268, 2020.
[42] Ziyu Xu, Chen Dan, Justin Khim, and Pradeep Ravikumar. Class-weighted classification: Trade-offs and robust approaches. In International Conference on Machine Learning, pages 10544–10554. PMLR, 2020.
[43] Liang Yan, Shengzhong Zhang, Bisheng Li, min zhou, and Zengfeng Huang. UNREAL: Unlabeled nodes retrieval and labeling for heavily-imbalanced node classification, 2023.
[44] Zhilin Yang, William Cohen, and Ruslan Salakhudinov. Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning, pages 40-48. PMLR, 2016.
[45] Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, and Yi Ma. Rethinking bias-variance trade-off for generalization of neural networks. In International Conference on Machine Learning, pages 10767-10777. PMLR, 2020.