[59] Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, and Stan Z Li. Progcl: Rethinking hard negative mining in graph contrastive learning. In ICML, 2022. 1, 3, 5, 15
[60] Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. Representation learning on graphs with jumping knowledge networks. In ICML, 2018. 16
[61] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? In ICLR, 2019. 17
[62] Pinar Yanardag and SVN Vishwanathan. Deep graph kernels. In SIGKDD, 2015. 16
[63] Chenxiao Yang, Qitian Wu, Jiahua Wang, and Junchi Yan. Graph neural networks are inherently good generalizers: Insights by bridging gnns and mlps. In ICLR, 2023. 7
[64] Yihang Yin, Qingzhong Wang, Siyu Huang, Haoyi Xiong, and Xiang Zhang. Autogcl: Automated graph contrastive learning via learnable view generators. In AAAI, 2022. 5, 15
[65] Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. Graph convolutional neural networks for web-scale recommender systems. In SIGKDD, 2018. 1
[66] Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. Graph contrastive learning with augmentations. In NeurIPS, 2020. 3, 5, 15, 17
[67] Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. Graph contrastive learning automated. In ICML, 2021. 5, 15
[68] Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen. Bringing your own view: Graph contrastive learning without prefabricated data augmentations. In WSDM, 2022. 3
[69] Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stephane Deny. Barlow twins: Self-supervised learning via redundancy reduction. In ICML, 2021. 3, 8
[70] Wiley ZELLNER. An introduction to Bayesian inference in econometrics. 1996. 10
[71] Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, and Philip S Yu. From canonical correlation analysis to self-supervised graph neural networks. In NeurIPS, 2021. 3, 7, 8, 9
[72] Qi Zhang, Yifei Wang, and Yisen Wang. How mask matters: Towards theoretical understandings of masked autoencoders. In NeurIPS, 2022. 3
[73] Qi Zhang, Yifei Wang, and Yisen Wang. On the generalization of multi-modal contrastive learning. In ICML, 2023. 3
[74] Shengzhong Zhang, Zengfeng Huang, Haicang Zhou, and Ziang Zhou. Sce: Scalable network embedding from sparsest cut. In SIGKDD, 2020. 3, 15
[75] Yifei Zhang, Hao Zhu, Zixing Song, Piotr Koniusz, and Irwin King. Spectral feature augmentation for graph contrastive learning and beyond. In AAAI, 2023. 3
[76] Yizhen Zheng, Shirui Pan, Vincent Cs Lee, Yu Zheng, and Philip S Yu. Rethinking and scaling up graph contrastive learning: An extremely efficient approach with group discrimination. In NeurIPS, 2022. 22
[77] Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131, 2020. 3, 5, 6, 8, 9, 15, 17
[78] Yanqiao Zhu, Yichen Xu, Qiang Liu, and Shu Wu. An empirical study of graph contrastive learning. In NeurIPS, 2021. 21
[79] Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. Graph contrastive learning with adaptive augmentation. In WWW, 2021. 3, 5, 9, 15, 17
[80] Zhijian Zhuo, Yifei Wang, Jinwen Ma, and Yisen Wang. Towards a unified theoretical understanding of non-contrastive learning via rank differential mechanism. In ICLR, 2023. 3, 7