[1] Muhammet Balcilar, Guillaume Renton, Pierre Héroux, Benoit Gaüzère, Sébastien Adam, and Paul Honeine. Analyzing the expressive power of graph neural networks in a spectral perspective. In 9th International Conference on Learning Representations, Virtual Event, Austria, May 3-7, 2021, 2021.
[2] Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al. Relational inductive biases, deep learning, and graph networks. ArXiv preprint, 2018.
[3] Daniel Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li Fei-Fei, Jiajun Wu, Josh Tenenbaum, and Daniel L. Yamins. Learning physical graph representations from visual scenes. 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.
[4] Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. Spectral networks and locally connected networks on graphs. In Yoshua Bengio and Yann LeCun, editors, 2nd International Conference on Learning Representations, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, 2014.
[5] Chen Cai, Dingkang Wang, and Yusu Wang. Graph coarsening with neural networks. In 9th International Conference on Learning Representations, Virtual Event, Austria, May 3-7, 2021, 2021.
[6] George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu. Dataset distillation by matching training trajectories. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2022, New Orleans, LA, USA, June 19-20, 2022, pages 4749-4758, 2022.
[7] George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. Generalizing dataset distillation via deep generative prior. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3739-3748, 2023.
[8] Yu Chen, Lingfei Wu, and Mohammed J. Zaki. Iterative deep graph learning for graph neural networks: Better and robust node embeddings. 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.
[9] Marco Cuturi. Sinkhorn distances: Lightspeed computation of optimal transport. In Christopher J. C. Burges, León Bottou, Zoubin Ghahramani, and Kilian Q. Weinberger, editors, Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States, pages 2292-2300, 2013.
[10] Évariste Daller, Sébastien Bougleux, Luc Brun, and Olivier Lézoray. Local patterns and supergraph for chemical graph classification with convolutional networks. In Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, August 17-19, 2018, Proceedings, volume 11004, pages 97-106, 2018.
[11] Tuan Van Dao, Hiroshi Sato, and Masao Kubo. Mlp-mixer-autoencoder: A lightweight ensemble architecture for malware classification. Inf., 14(3):167, 2023.
[12] Kinkar Ch. Das, Seyed Ahmad Mojallal, and Vilmar Trevisan. Distribution of laplacian eigenvalues of graphs. Linear Algebra and its Applications, 508:48-61, 2016.
[13] Michael Defferrard, Xavier Bresson, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett, editors, Advances in Neural Information