[22] William L. Hamilton, Rex Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems, 2018.
[23] Wei Hu, Lechao Xiao, and Jeffrey Pennington. Provable benefit of orthogonal initialization in optimizing deep linear networks. In International Conference on Machine Learning, 2020.
[24] Arthur Jacot, Franck Gabriel, and Clément Hongler. Neural tangent kernel: Convergence and generalization in neural networks. In Advances in Neural Information Processing Systems, 2018.
[25] Ajay Jaiswal, Peihao Wang, Tianlong Chen, Justin F. Rousseau, Ying Ding, and Zhangyang Wang. Old can be gold: Better gradient flow can make vanilla-gcns great again. In Advances in Neural Information Processing Systems, 2022.
[26] Stefanie Jegelka. Theory of graph neural networks: Representation and learning. In The International Congress of Mathematicians, 2022.
[27] S. Kearnes, K. McCloskey, M. Berndl, V. Pande, and P. Riley. Molecular graph convolutions: Moving beyond fingerprints. In Journal of Computer-Aided Molecular Design, 2016.
[28] Dongkwan Kim and Alice Oh. How to find your friendly neighborhood: Graph attention design with self-supervision. In International Conference on Learning Representations, 2021.
[29] Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations, 2017.
[30] Boris Knyazev, Graham W. Taylor, and Mohamed R. Amer. Understanding attention and generalization in graph neural networks. In Advances in Neural Information Processing Systems, 2019.
[31] D. Kunin, J. Sagastuy-Brena, S. Ganguli, D.L.K. Yamins, and H. Tanaka. Neural mechanics: Symmetry and broken conservation laws in deep learning dynamics. In International Conference on Learning Representations, 2021.
[32] Thien Le and Stefanie Jegelka. Training invariances and the low-rank phenomenon: beyond linear networks. In International Conference on Learning Representations, 2022.
[33] Guohao Li, Matthias Müller, Bernard Ghanem, and Vladlen Koltun. Training graph neural networks with 1000 layers. In International Conference on Machine Learning, 2022.
[34] Guohao Li, Matthias Müller, Ali Thabet, and Bernard Ghanem. Deep GCs: Can GCs go as deep as cnns? In International Conference on Computer Vision, 2019.
[35] Qimai Li, Zhichao Han, and Xiao-Ming Wu. Deeper insights into graph convolutional networks for semi-supervised learning. In AAAI Conference on Artificial Intelligence, 2018.
[36] Meng Liu, Hongyang Gao, and Shuiwang Ji. Towards deeper graph neural networks. In SIGKDD International Conference on Knowledge Discovery and Data Mining, 2020.
[37] Dmytro Mishkin and Jiri Matas. All you need is a good init. In International Conference on Learning Representations, 2016.
[38] Pál András Papp, Karolis Martinkus, Lukas Faber, and Roger Wattenhofer. Dropgnn: Random dropouts increase the expressiveness of graph neural networks. In Advances in Neural Information Processing Systems, 2021.
[39] Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. Geom-gcn: Geometric graph convolutional networks. In International Conference on Learning Representations, 2020.
[40] Oleg Platonov, Denis Kuznedelev, Michael Diskin, Artem Babenko, and Liudmila Prokhorenkova. A critical look at the evaluation of gnns under heterophily: are we really making progress? In International Conference on Learning Representations, 2023.