[51] Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, and Stephan Gümnmann. Robustness of graph neural networks at scale. Advances in Neural Information Processing Systems, 34:7637-7649, 2021.
[52] Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L Dyer, Remi Munos, Petar Velicković, and Michal Valko. Large-scale representation learning on graphs via bootstrapping. In International Conference on Learning Representations, 2022.
[53] Aditya Grover and Jure Leskovec. Node2vec: Scalable feature learning for networks. In the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2016.
[54] Thomas N Kipf and Max Welling. Variational graph auto-encoders. NIPS Workshop on Bayesian Deep Learning, 2016.
[55] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning, 2020.
[56] Ralph Linsker. Self-organization in a perceptual network. Computer, 21(3):105-117, 1988.
[57] Jordan T Ash, Surbhi Goel, Akshay Krishnamurthy, and Dipendra Misra. Investigating the role of negatives in contrastive representation learning. arXiv preprint arXiv:2106.09943, 2021.
[58] Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. Adversarial graph augmentation to improve graph contrastive learning. In Advances in Neural Information Processing Systems, 2021.
[59] Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. Molecule: a benchmark for molecular machine learning. Chemical science, 9(2):513-530, 2018.
[60] Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, and Jie Tang. Tdgia: Effective injection attacks on graph neural networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 2461-2471, 2021.
[61] Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, and James Cheng. Understanding and improving graph injection attack by promoting unnoticeability. In International Conference on Learning Representations, 2022.
[62] Jean-Bastien Grill, Florian Strub, Florent Alché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. In Advances in neural information processing systems, pages 21271-21284, 2020.
[63] R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. Learning deep representations by mutual information estimation and maximization. In International Conference on Learning Representations, 2019.
[64] Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu. Adversarial examples for graph data: Deep insights into attack and defense. In *IJCAI*, pages 4816-4823, 7 2019.
[65] Yanqiao Zhu, Yichen Xu, Qiang Liu, and Shu Wu. An Empirical Study of Graph Contrastive Learning. arXiv.org, September 2021.
[66] Sihang Li, Xiang Wang, An Zhang, Xiangnan He, and Tat-Seng Chua. Let invariant rationale discovery inspire graph contrastive learning. In Proceedings of the 39th International Conference on Machine Learning, 2022.