[16] A. Garriga-Alonso, C. E. Rasmussen, and L. Aitchison. Deep convolutional networks as shallow gaussian processes. arXiv preprint arXiv:1808.05587, 2018.
[17] C. R. Givens and R. M. Shortt. A class of Wasserstein metrics for probability distributions. Michigan Mathematical Journal, 31(2):231-240, 1984.
[18] W. L. Hamilton. Graph representation learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 14(3):1-159, 2020.
[19] W. L. Hamilton, R. Ying, and J. Leskovec. Inductive representation learning on large graphs. arXiv, abs/1706.02216, 2017.
[20] T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman. The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer, 2009.
[21] M. He, Z. Wei, H. Xu, et al. Bernnet: Learning arbitrary graph spectral filters via bernstein approximation. Advances in Neural Information Processing Systems, 34, 2021.
[22] T. Hofmann, B. Scholkopf, and A. J. Smola. Kernel methods in machine learning1. The Annals of Statistics, 36(3):1171-1220, 2008.
[23] C. Hua, G. Rabusseau, and J. Tang. High-order pooling for graph neural networks with tensor decomposition. arXiv preprint arXiv:2205.11691, 2022.
[24] H. Jeffreys. An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 186(1007):453-461, 1946.
[25] H. Jeffreys. The theory of probability. OuP Oxford, 1998.
[26] D. Jin, R. Wang, M. Ge, D. He, X. Li, W. Lin, and W. Zhang. Raw-gnn: Random walk aggregation based graph neural network. arXiv preprint arXiv:2206.13953, 2022.
[27] T. N. Kipf and M. Welling. Semi-supervised classification with graph convolutional networks. arXiv, abs/1609.02907, 2016.
[28] M. Knott and C. S. Smith. On the optimal mapping of distributions. Journal of Optimization Theory and Applications, 43:39-49, 1984.
[29] K. Koutroumbas and S. Theodoridis. Pattern recognition. Academic Press, 2008.
[30] J. Lee, Y. Bahri, R. Novak, S. S. Schoenholz, J. Pennington, and J. Sohl-Dickstein. Deep neural networks as gaussian processes. arXiv preprint arXiv:1711.00165, 2017.
[31] X. Li, R. Zhu, Y. Cheng, C. Shan, S. Luo, D. Li, and W. Qian. Finding global homophily in graph neural networks when meeting heterophily. arXiv preprint arXiv:2205.07308, 2022.
[32] D. Lim, X. Li, F. Hohne, and S.-N. Lim. New benchmarks for learning on non-homophilous graphs. arXiv preprint arXiv:2104.01404, 2021.
[33] S. Luan, C. Hua, Q. Lu, J. Zhu, X.-W. Chang, and D. Precup. When do we need graph neural networks for node classification? International Conference on Complex Networks and Their Applications, 2023.
[34] S. Luan, C. Hua, Q. Lu, J. Zhu, M. Zhao, S. Zhang, X.-W. Chang, and D. Precup. Is heterophily a real nightmare for graph neural networks to do node classification? arXiv preprint arXiv:2109.05641, 2021.
[35] S. Luan, C. Hua, Q. Lu, J. Zhu, M. Zhao, S. Zhang, X.-W. Chang, and D. Precup. Revisiting heterophily for graph neural networks. Advances in neural information processing systems, 35:1362-1375, 2022.
[36] S. Luan, M. Zhao, X.-W. Chang, and D. Precup. Break the ceiling: Stronger multi-scale deep graph convolutional networks. Advances in neural information processing systems, 32, 2019.
[37] S. Luan, M. Zhao, C. Hua, X.-W. Chang, and D. Precup. Complete the missing half: Augmenting aggregation filtering with diversification for graph convolutional networks. In NeurIPS 2022 Workshop: New Frontiers in Graph Learning, 2022.
[38] Y. Ma, X. Liu, N. Shah, and J. Tang. Is homophily a necessity for graph neural networks? arXiv preprint arXiv:2106.06134, 2021.
[39] T. Maehara. Revisiting graph neural networks: All we have is low-pass filters. arXiv preprint arXiv:1905.09550, 2019.