[59] Xuhong Wang, Ding Lyu, Mengjian Li, Yang Xia, Qi Yang, Xinwen Wang, Xinguang Wang, Ping Cui, Yupu Yang, Bowen Sun, and Zhenyu Guo. APAN: asynchronous propagation attention network for real-time temporal graph embedding. In International Conference on Management of Data, pages 2628-2638. ACM, 2021.
[60] Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, and Pan Li. Inductive representation learning in temporal networks via causal anonymous walks. In 9th International Conference on Learning Representations. OpenReview.net, 2021.
[61] Jason Weston, Sumit Chopra, and Antoine Bordes. Memory networks. In 3rd International Conference on Learning Representations, 2015.
[62] Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. In Advances in Neural Information Processing Systems 34, pages 22419–22430, 2021.
[63] Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. Graph wavenet for deep spatial-temporal graph modeling. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pages 1907-1913. ijcai.org, 2019.
[64] Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. Inductive representation learning on temporal graphs. In 8th International Conference on Learning Representations. OpenReview.net, 2020.
[65] Guotong Xue, Ming Zhong, Jianxin Li, Jia Chen, Chengshuai Zhai, and Ruochen Kong. Dynamic network embedding survey. Neurocomputing, 472:212-223, 2022.
[66] Jiaxuan You, Tianyu Du, and Jure Leskovec. ROLAND: graph learning framework for dynamic graphs. In The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 2358-2366. ACM, 2022.
[67] Bing Yu, Haoteng Yin, and Zhanxing Zhu. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pages 3634-3640. ijcai.org, 2018.
[68] Le Yu. An empirical evaluation of temporal graph benchmark. arXiv preprint arXiv:2307.12510, 2023.
[69] Le Yu, Bowen Du, Xiao Hu, Leilei Sun, Liangzhe Han, and Weifeng Lv. Deep spatio-temporal graph convolutional network for traffic accident prediction. Neurocomputing, 423:135-147, 2021.
[70] Le Yu, Zihang Liu, Leilei Sun, Bowen Du, Chuanren Liu, and Weifeng Lv. Continuous-time user preference modelling for temporal sets prediction. IEEE Transactions on Knowledge and Data Engineering, 2023.
[71] Le Yu, Guanghui Wu, Leilei Sun, Bowen Du, and Weifeng Lv. Element-guided temporal graph representation learning for temporal sets prediction. In The ACM Web Conference 2022, pages 1902-1913. ACM, 2022.
[72] Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, and Liang Wang. Dynamic graph neural networks for sequential recommendation. IEEE Transactions on Knowledge and Data Engineering, 2022.
[73] Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Thirty-Fifth AAAI Conference on Artificial Intelligence, pages 11106-11115. AAAI Press, 2021.
[74] Hongkuan Zhou, Da Zheng, Israt Nisa, Vasileios Ioannidis, Xiang Song, and George Karypis. Tgl: a general framework for temporal gnn training on billion-scale graphs. Proceedings of the VLDB Endowment, 15(8):1572-1580, 2022.