[42] Austin R. Benson, David F. Gleich, and Lek-Heng Lim. The spacey random walk: A stochastic process for higher-order data. SIAM Review, 59(2):321-345, 2017. doi: 10.1137/16M1074023. URL https://doi.org/10.1137/16M1074023.
[43] Ankit Sharma, Shafiq Joty, Himanshu Kharkwal, and Jaideep Srivastava. Hyperedge2vec: Distributed representations for hyperedges, 2018. URL https://openreview.net/forum?id=rJ5C67-C-.
[44] Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Peter Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy. MLP-mixer: An all-MLP architecture for vision. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021. URL https://openreview.net/forum?id=EI2K0XKdnP.
[45] Silvio Micali and Zeyuan Allen Zhu. Reconstructing markov processes from independent and anonymous experiments. Discrete Applied Mathematics, 200:108-122, 2016. ISSN 0166-218X. doi: https://doi.org/10.1016/j.dam.2015.06.035. URL https://www.sciencedirect.com/science/article/pii/S0166218X15003212.
[46] Ali Behrouz and Margo Seltzer. ADMIRE++: Explainable anomaly detection in the human brain via inductive learning on temporal multiplex networks. In ICML 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH), 2023. URL https://openreview.net/forum?id=t4H8acYudJ.
[47] Joakim Skarding, Bogdan Gabrys, and Katarzyna Musial. Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey. IEEE Access, 9:79143-79168, 2021.
[48] Youngjoo Seo, Michael Defferrard, Pierre Vandergheynst, and Xavier Bresson. Structured sequence modeling with graph convolutional recurrent networks. In International conference on neural information processing, pages 362-373. Springer, 2018.
[49] Ling Zhao, Yu jiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li. T-gcn: A temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 21(9):3848-3858, 2019.
[50] Hao Peng, Hongfei Wang, Bowen Du, Md Zakirul Alam Bhuiyan, Hongyuan Ma, Jianwei Liu, Lihong Wang, Zeyu Yang, Linfeng Du, Senzhang Wang, et al. Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting. Information Sciences, 521:277-290, 2020.
[51] Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, and Jian Yu. Traffic flow prediction via spatial temporal graph neural network. In Proceedings of The Web Conference 2020, pages 1082-1092, 2020.
[52] Jiaxuan You, Tianyu Du, and Jure Leskovec. Roland: Graph learning framework for dynamic graphs. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '22, page 2358-2366, New York, NY, USA, 2022. Association for Computing Machinery. ISBN 9781450393850. doi: 10.1145/3534678.3539300. URL https://doi.org/10.1145/3534678.3539300.
[53] Farnoosh Hashemi, Ali Behrouz, and Milad Rezaei Hajidehi. Cs-tgn: Community search via temporal graph neural networks. In *Companion Proceedings of the Web Conference* 2023, WWW '23, New York, NY, USA, 2023. Association for Computing Machinery. doi: 10.1145/3543873.3587654. URL https://doi.org/10.1145/3543873.3587654.
[54] Srijan Kumar, Xikun Zhang, and Jure Leskovec. Predicting dynamic embedding trajectory in temporal interaction networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '19, page 1269-1278, New York, NY, USA, 2019. Association for Computing Machinery. ISBN 9781450362016. doi: 10.1145/3292500.3330895. URL https://doi.org/10.1145/3292500.3330895.