[87] Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao Wang, and Yang You. Cafe: Learning to condense dataset by aligning features. In CVPR, 2022.
[88] Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. Dataset distillation. ArXiv preprint, 2018.
[89] Yili Wang, Kaixiong Zhou, Rui Miao, Ninghao Liu, and Xin Wang. Adagcl: Adaptive subgraph contrastive learning to generalize large-scale graph training.
[90] Max Welling. Herding dynamical weights to learn. In Andrea Pohoreckiy Danyluk, Léon Botou, and Michael L. Littman, editors, Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, Quebec, Canada, June 14-18, 2009, volume 382 of ACM International Conference Proceeding Series, pages 1121-1128, 2009.
[91] Gert W. Wolf. Facility location: concepts, models, algorithms and case studies. series: Contributions to management science. Int. J. Geogr. Inf. Sci., 25(2):331-333, 2011.
[92] Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. Simplifying graph convolutional 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 6861-6871, 2019.
[93] Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S Yu. A comprehensive survey on graph neural networks. ArXiv preprint, 2019.
[94] Xinyue Xia, Gal Mishne, and Yusu Wang. Implicit graphon neural representation. In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics., 2023.
[95] Hongteng Xu, Dixin Luo, Lawrence Carin, and Hongyuan Zha. Learning graphons via structured gromov-wasserstein barycenters. In Thirty-Fifth AAAI Conference on Artificial Intelligence, Virtual Event, February 2-9, 2021, pages 10505-10513, 2021.
[96] Shuo Yang, Zeke Xie, Hanyu Peng, Min Xu, Mingming Sun, and Ping Li. Dataset pruning: Reducing training data by examining generalization influence. ArXiv preprint, abs/2205.09329, 2022.
[97] Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov. Revisiting semi-supervised learning with graph embeddings. In Maria-Florina Balcan and Kilian Q. Weinberger, editors, Proceedings of the 33nd International Conference on Machine Learning, New York City, NY, USA, June 19-24, 2016, volume 48 of JMLR Workshop and Conference Proceedings, pages 40-48, 2016.
[98] Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. Graph convolutional neural networks for web-scale recommender systems. In Yike Guo and Faisal Farooq, editors, Proceedings of the 24th ACM International Conference on Knowledge Discovery & Data Mining, London, UK, August 19-23, 2018, pages 974-983, 2018.
[99] Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, and Shuiwang Ji. Graphfm: Improving large-scale GNN training via feature momentum. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, and Sivan Sabato, editors, International Conference on Machine Learning, 17-23 July 2022, Baltimore, Maryland, USA, volume 162 of Proceedings of Machine Learning Research, pages 25684-25701, 2022.
[100] Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor K. Prasanna. Graphsaint: Graph sampling based inductive learning method. In 8th International Conference on Learning Representations, Addis Ababa, Ethiopia, April 26-30, 2020, 2020.
[101] Muhan Zhang and Yixin Chen. Link prediction based on graph neural networks. In Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, and Roman Garnett, editors, Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, December 3-8, 2018, Montréal, Canada, pages 5171-5181, 2018.