C. Shannon. The zero error capacity of a noisy channel. IRE Transactions on Information Theory, 2(3):8-19, 1956.
Nino Shervashidze, SVN Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten Borgwardt. Efficient graphlet kernels for large graph comparison. In Artificial intelligence and statistics, pages 488-495. PMLR, 2009.
Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research, 12(9), 2011.
Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, and Michalis Vazirgiannis. Grakel: A graph kernel library in python. The Journal of Machine Learning Research, 21(1):1993-1997, 2020.
Simon Stahlberg, Blai Bonet, and Hector Geffner. Learning general optimal policies with graph neural networks: Expressive power, transparency, and limits. In Proceedings of the International Conference on Automated Planning and Scheduling, volume 32, pages 629-637, 2022.
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000, 2019.
Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin Wang. Gppt: Graph pre-training and prompt tuning to generalize graph neural networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1717-1727, 2022.
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. Adversarial graph augmentation to improve graph contrastive learning. Advances in Neural Information Processing Systems, 34:15920-15933, 2021.
Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan, Yaoqing Yang, and Danai Koutra. Augmentations in graph contrastive learning: Current methodological flaws & towards better practices. In Proceedings of the ACM Web Conference 2022, pages 1538-1549, 2022.
Petar Velickovic, William Fedus, William L Hamilton, Pietro Lio, Yoshua Bengio, and R Devon Hjelm. Deep graph infomax. ICLR (Poster), 2(3):4, 2019.
Saurabh Verma and Zhi-Li Zhang. Learning universal graph neural network embeddings with aid of transfer learning. arXiv preprint arXiv:1909.10086, 2019.
Chunyu Wei, Jian Liang, Di Liu, and Fei Wang. Contrastive graph structure learning via information bottleneck for recommendation. Advances in Neural Information Processing Systems, 35:20407-20420, 2022.
Boris Weisfeiler and Andrei Leman. The reduction of a graph to canonical form and the algebra which appears therein. NTI, Series, 2(9):12-16, 1968.
Henry Wolkowicz, Romesh Saigal, and Lieven Vandenberghe. Handbook of semidefinite programming: theory, algorithms, and applications, volume 27. Springer Science & Business Media, 2012.
Tailin Wu, Hongyu Ren, Pan Li, and Jure Leskovec. Graph information bottleneck. Advances in Neural Information Processing Systems, 33:20437-20448, 2020.
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. Self-supervised graph learning for recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pages 726-735, 2021.
Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, and Le Song. Graph neural networks. Springer, 2022.
Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, and Stan Z Li. Simgrace: A simple framework for graph contrastive learning without data augmentation. In Proceedings of the ACM Web Conference 2022, pages 1070-1079, 2022.