[1] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018.
[2] Petar Velicković, William Fedus, William L Hamilton, Pietro Lio, Yoshua Bengio, and R Devon Hjelm. Deep graph infomax. In ICLR, 2018.
[3] Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. Graph contrastive learning with augmentations. NeurIPS, pages 5812-5823, 2020.
[4] Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. Graph contrastive learning with adaptive augmentation. In The Web Conference, pages 2069-2080, 2021.
[5] Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L Dyer, Remi Munos, Petar Velickovic, and Michal Valko. Large-scale representation learning on graphs via bootstrapping. In ICLR, 2021.
[6] Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, and Philip S Yu. From canonical correlation analysis to self-supervised graph neural networks. NeurIPS, 2021.
[7] Puja Trivedi, Ekdeep S Lubana, Mark Heimann, Danai Koutra, and Jayaraman Thiagarajan. Analyzing data-centric properties for graph contrastive learning. NeurIPS, 2022.
[8] Yanqiao Zhu, Yichen Xu, Qiang Liu, and Shu Wu. An empirical study of graph contrastive learning. arXiv preprint arXiv:2109.01116, 2021.
[9] Thomas N Kipf and Max Welling. Variational graph auto-encoders. ArXiv, 2016.
[10] William L Hamilton, Rex Ying, and Jure Leskovec. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584, 2017.
[11] Hao Zhu, Ke Sun, and Peter Koniusz. Contrastive laplacian eigenmaps. NeurIPS, 2021.
[12] Hengrui Zhang, Qitian Wu, Yu Wang, Shaofeng Zhang, Junchi Yan, and Philip S Yu. Localized contrastive learning on graphs. arXiv preprint arXiv:2212.04604, 2022.
[13] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In KDD, pages 855-864, 2016.
[14] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiao zhu Mei. Line: Large-scale information network embedding. In WWW, pages 1067-1077, 2015.
[15] Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. Beyond homophily in graph neural networks: Current limitations and effective designs. NeurIPS, pages 7793–7804, 2020.
[16] Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser Nam Lim. Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods. NeurIPS, pages 20887-20902, 2021.
[17] Mingyue Tang, Pan Li, and Carl Yang. Graph auto-encoder via neighborhood Wasserstein reconstruction. In ICLR, 2021.
[18] Teng Xiao, Zhengyu Chen, Zhimeng Guo, Zeyang Zhuang, and Suhang Wang. Decoupled self-supervised learning for non-homophilous graphs. arXiv preprint arXiv:2206.03601, 2022.
[19] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In ICML, pages 1597-1607, 2020.
[20] Nian Liu, Xiao Wang, Deyu Bo, Chuan Shi, and Jian Pei. Revisiting graph contrastive learning from the perspective of graph spectrum. In NeurIPS, 2022.
[21] Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. Adaptive universal generalized pagerank graph neural network. In ICLR, 2020.