[1] A.-L. Barabási. Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1987):20120375, 2013.
[2] T. Bertin-Mahieux, D. P. Ellis, B. Whitman, and P. Lamere. The million song dataset. 2011.
[3] I. I. Bogoch, A. Watts, A. Thomas-Bachli, C. Huber, M. U. Kraemer, and K. Khan. Potential for global spread of a novel coronavirus from china. Journal of travel medicine, 27(2):taaa011, 2020.
[4] A. D. Broido and A. Clauset. Scale-free networks are rare. Nature communications, 10(1):1017, 2019.
[5] B. P. Chamberlain, S. Shirobokov, E. Rossi, F. Frasca, T. Markovich, N. Y. Hammerla, M. M. Bronstein, and M. Hansmire. Graph neural networks for link prediction with subgraph sketching. In The Eleventh International Conference on Learning Representations, 2022.
[6] S. Chanpuriya, R. A. Rossi, S. Kim, T. Yu, J. Hoffswell, N. Lipka, S. Guo, and C. Musco. Direct embedding of temporal network edges via time-decayed line graphs. arXiv preprint arXiv:2210.00032, 2022.
[7] Z. Chen, S. Villar, L. Chen, and J. Bruna. On the equivalence between graph isomorphism testing and function approximation with gnns. Advances in neural information processing systems, 32, 2019.
[8] A. Cini, I. Marisca, F. M. Bianchi, and C. Alippi. Scalable spatiotemporal graph neural networks. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 7218-7226, 2023.
[9] W. Cong, S. Zhang, J. Kang, B. Yuan, H. Wu, X. Zhou, H. Tong, and M. Mahdavi. Do we really need complicated model architectures for temporal networks? In The Eleventh International Conference on Learning Representations.
[10] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009.
[11] X. Ding, S. Huang, A. Leung, and R. Rabbany. Incorporating dynamic flight network in seir to model mobility between populations. Applied Network Science, 6(1):1-24, 2021.
[12] V. P. Dwivedi and X. Bresson. A generalization of transformer networks to graphs. arXiv preprint arXiv:2012.09699, 2020.
[13] J. Gao and B. Ribeiro. On the equivalence between temporal and static equivariant graph representations. In International Conference on Machine Learning, pages 7052-7076. PMLR, 2022.
[14] E. Hajiramezanali, A. Hasanzadeh, K. Narayanan, N. Duffield, M. Zhou, and X. Qian. Variational graph recurrent neural networks. Advances in neural information processing systems, 32, 2019.
[15] B. Hidasi and D. Tikk. Fast als-based tensor factorization for context-aware recommendation from implicit feedback. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part II 23, pages 67-82. Springer, 2012.
[16] W. Hu, M. Fey, H. Ren, M. Nakata, Y. Dong, and J. Leskovec. Ogb-lsc: A large-scale challenge for machine learning on graphs. arXiv preprint arXiv:2103.09430, 2021.
[17] W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, 33:22118-22133, 2020.