Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 3837-3845, 2016.
[14] Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, and Zhuo Feng. Graphzoom: A multi-level spectral approach for accurate and scalable graph embedding. In 8th International Conference on Learning Representations, Addis Ababa, Ethiopia, April 26-30, 2020, 2020.
[15] Yihe Dong and Will Sawin. COPT: coordinated optimal transport on graphs. In Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, December 6-12, 2020, virtual, 2020.
[16] Holger Ebel and Stefan Bornholdt. Coevolutionary games on networks. Physical Review E, 66(5):056118, 2002.
[17] Justin Eldridge, Mikhail Belkin, and Yusu Wang. Graphons, mergeons, and so on! In Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett, editors, Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2307-2315, 2016.
[18] Matthias Fey and Jan Eric Lenssen. Fast graph representation learning with pytorch geometric. ArXiv preprint, 2019.
[19] Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He. Learning discrete structures for graph neural networks. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 1972-1982, 2019.
[20] Alan Frieze and Ravi Kannan. Quick approximation to matrices and applications. Combinatorica, 19(2):175-220, 1999.
[21] Shuang Gao and Peter E Caines. Graphon control of large-scale networks of linear systems. IEEE Transactions on Automatic Control, 2019.
[22] Jiahui Geng, Zongxiong Chen, Yuandou Wang, Herbert Woisetschlaeger, Sonja Schimmler, Ruben Mayer, Zhiming Zhao, and Chunming Rong. A survey on dataset distillation: Approaches, applications and future directions. ArXiv preprint, abs/2305.01975, 2023.
[23] Jianyang Gu, Kai Wang, Wei Jiang, and Yang You. Summarizing stream data for memory-restricted online continual learning. ArXiv preprint, abs/2305.16645, 2023.
[24] Ziyao Guo, Kai Wang, George Cazenavette, Hui Li, Kaipeng Zhang, and Yang You. Towards lossless dataset distillation via difficulty-aligned trajectory matching. ArXiv preprint, abs/2310.05773, 2023.
[25] Ivan Gutman and Bo Zhou. Laplacian energy of a graph. Linear Algebra and its Applications, 414(1):29-37, 2006.
[26] William L. Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 1024-1034, 2017.
[27] Peter D Hoff, Adrian E Raftery, and Mark S Handcock. Latent space approaches to social network analysis. Journal of the american Statistical association, 97(460):1090-1098, 2002.
[28] Paul W Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt. Stochastic blockmodels: First steps. Social networks, 5(2):109-137, 1983.