[64] Puja Trivedi, Ekdeep S Lubana, Mark Heimann, Danai Koutra, and Jayaraman Thiagarajan. Analyzing data-centric properties for graph contrastive learning. NeurIPS, 35:14030-14043, 2022.
[65] Petar Velickovic, William Fedus, William L Hamilton, Pietro Lio, Yoshua Bengio, and R. Devon Hjelm. Deep graph infomax. In ICLR, 2019.
[66] S. V. N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, and Karsten M. Borgwardt. Graph kernels. Journal of Machine Learning Research, 11:1201-1242, 2010.
[67] Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, and Jing Jiang. MGAE: Marginalized graph autoencoder for graph clustering. In CIKM, pages 889-898, 2017.
[68] Mike Wu and Noah Goodman. A simple framework for uncertainty in contrastive learning. arXiv preprint arXiv:2010.02038, 2020.
[69] Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, and Xiang Zhang. Infogcl: Information-aware graph contrastive learning. In NeurIPS, pages 30414-30425, 2021.
[70] Jiarong Xu, Yang Yang, Junru Chen, Xin Jiang, Chunping Wang, Jiangang Lu, and Yizhou Sun. Unsupervised adversarially robust representation learning on graphs. In AAAI, pages 4290-4298, 2022.
[71] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks. In ICLR, 2019.
[72] Xi Yan, David Acuna, and Sanja Fidler. Neural data server: A large-scale search engine for transfer learning data. In CVPR, pages 3893-3902, 2020.
[73] Cheng Yang, Deyu Bo, Jixi Liu, Yufei Peng, Boyu Chen, Haoran Dai, Ao Sun, Yue Yu, Yixin Xiao, Qi Zhang, et al. Data-centric graph learning: A survey. arXiv preprint arXiv:2310.04987, 2023.
[74] Yi Yang, Zhigang Ma, Feiping Nie, Xiaojun Chang, and Alexander G Hauptmann. Multi-class active learning by uncertainty sampling with diversity maximization. IJCV, 113:113-127, 2015.
[75] Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. Graph contrastive learning automated. In ICML, pages 12121-12132, 2021.
[76] Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. Graph contrastive learning with augmentations. In NeurIPS, pages 5812-5823, 2020.
[77] Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen. When does self-supervision help graph convolutional networks. In ICML, pages 10871-10880, 2020.
[78] Jiaqi Zeng and Pengtao Xie. Contrastive self-supervised learning for graph classification. In AAAI, pages 10824-10832, 2021.
[79] Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, and Xia Hu. Data-centric ai: Perspectives and challenges. In SDM, pages 945-948. SIAM, 2023.
[80] Jie Zhang, Yuxiao Dong, Yan Wang, Jie Tang, and Ming Ding. Prone: Fast and scalable network representation learning. In *IJCAI*, pages 4278–4284, 2019.
[81] Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen. An end-to-end deep learning architecture for graph classification. In AAAI, page 4438-4445, 2018.
[82] Wentao Zhang, Yu Shen, Yang Li, Lei Chen, Zhi Yang, and Bin Cui. ALG: fast and accurate active learning framework for graph convolutional networks. In SIGMOD, pages 2366-2374, 2021.
[83] Zaixin Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee. Motif-based graph self-supervised learning for molecular property prediction. In NeurIPS, pages 15870-15882, 2021.
[84] Jingbo Zhu, Huizhen Wang, Tianshun Yao, and Benjamin K. Tsou. Active learning with sampling by uncertainty and density for word sense disambiguation and text classification. In COLING, pages 1137-1144, 2008.
[85] Yanqiao Zhu, Yichen Xu, Qiang Liu, and Shu Wu. An empirical study of graph contrastive learning. In NeurIPS D&B, 2021.
[86] Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131, 2020.