[40] Edouard Pauwels and Samuel Vaiter. The derivatives of sinkhorn-knopp converge. SIAM Journal on Optimization, 33(3):1494-1517, 2023.
[41] Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, and Junzhou Huang. Graph representation learning via graphical mutual information maximization. In Proceedings of The Web Conference 2020, pages 259–270, 2020.
[42] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social representations. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 701-710, 2014.
[43] Gabriel Peyré, Marco Cuturi, and Justin Solomon. Gromov-wasserstein averaging of kernel and distance matrices. In Proceedings of the International Conference on Machine Learning, pages 2664-2672. PMLR, 2016.
[44] Gabriel Peyré, Marco Cuturi, et al. Computational optimal transport: With applications to data science. Foundations and Trends® in Machine Learning, 11(5-6):355–607, 2019.
[45] Meyer Schetbon and Marco Cuturi. Linear time sinkhorn divergences using positive features. Advances in Neural Information Processing Systems, 33:13468-13480, 2020.
[46] Thibault Séjourne, François-Xavier Vialard, and Gabriel Peyre. The unbalanced gromov Wasserstein distance: Conic formulation and relaxation. Advances in Neural Information Processing Systems, 34:8766-8779, 2021.
[47] Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Gunnemann. Pitfalls of graph neural network evaluation. arXiv:1811.05868, 2018.
[48] Jian Shen, Yanru Qu, Weinan Zhang, and Yong Yu. Wasserstein distance guided representation learning for domain adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.
[49] 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.
[50] 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.
[51] Justin Solomon, Fernando De Goes, Gabriel Peyre, Marco Cuturi, Adrian Butscher, Andy Nguyen, Tao Du, and Leonidas Guibas. Convolutional Wasserstein distances: Efficient optimal transportation on geometric domains. ACM Transactions on Graphics, 34(4):1-11, 2015.
[52] Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv:1908.01000, 2019.
[53] 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.
[54] Jianheng Tang, Weiqi Zhang, Jiajin Li, Kangfei Zhao, Fugee Tsung, and Jia Li. Robust attributed graph alignment via joint structure learning and optimal transport. arXiv preprint arXiv:2301.12721, 2023.
[55] 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.
[56] Vayer Titouan, Nicolas Courty, Romain Tavenard, and Rémi Flamary. Optimal transport for structured data with application on graphs. In Proceedings of the International Conference on Machine Learning, pages 6275–6284. PMLR, 2019.
[57] Vayer Titouan, Ievgen Redko, Rémi Flamary, and Nicolas Courty. Co-optimal transport. Advances in Neural Information Processing Systems, 33:17559-17570, 2020.
[58] Petar Velickovic, William Fedus, William L Hamilton, Pietro Lio, Yoshua Bengio, and R Devon Hjelm. Deep graph infomax. International Conference on Learning Representations, 2019.
[59] Cédric Villani et al. Optimal transport: old and new, volume 338. Springer, 2009.