[60] Jovana Mitrovic, Brian McWilliams, Jacob C. Walker, Lars Holger Buesing, and Charles Blundell. Representation learning via invariant causal mechanisms. In 9th International Conference on Learning Representations. OpenReview.net, 2021.
[61] Diego C Nascimento, Bruno A Pimentel, Renata MCR Souza, Lilia Costa, Sandro Gonçalves, and Francisco Louzada. Dynamic graph in a symbolic data framework: An account of the causal relation using Covid-19 reports and some reflections on the financial world. Chaos, Solitons & Fractals, 153:111440, 2021.
[62] Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B. Schardl, and Charles E. Leiserson. Evolvecn: Evolving graph convolutional networks for dynamic graphs. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, pages 5363-5370. AAAI Press, 2020.
[63] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32, 2019.
[64] Pearl and Judea. Causal inference in statistics: An overview. Statistics Surveys, 3:96-146, 2009.
[65] Judea Pearl. Causal inference. Causality: Objectives and Assessment, pages 39-58, 2010.
[66] Judea Pearl and Dana Mackenzie. The Book of Why: the New Science of Cause and Effect. Basic books, 2018.
[67] Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. Elements of causal inference: foundations and learning algorithms. The MIT Press, 2017.
[68] Anton V Proskurnikov and Roberto Tempo. A tutorial on modeling and analysis of dynamic social networks. part i. Annual Reviews in Control, 43:65-79, 2017.
[69] Stephen Ranshous, Shitian Shen, Danai Koutra, Steve Harenberg, Christos Faloutsos, and Nagiza F Samata-ova. Anomaly detection in dynamic networks: a survey. Wiley Interdisciplinary Reviews: Computational Statistics, 7(3):223–247, 2015.
[70] John F Roddick and Myra Spiliopoulou. A bibliography of temporal, spatial and spatio-temporal data mining research. ACM SIGKDD Explorations Newsletter, 1(1):34-38, 1999.
[71] Mateo Rojas-Carulla, Bernhard Scholkopf, Richard Turner, and Jonas Peters. Invariant models for causal transfer learning. The Journal of Machine Learning Research, 19(1):1309-1342, 2018.
[72] Elan Rosenfeld, Pradeep Kumar Ravikumar, and Andrej Risteski. The risks of invariant risk minimization. In 9th International Conference on Learning Representations. OpenReview.net, 2021.
[73] Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637, 2020.
[74] Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. arXiv preprint arXiv:1911.08731, 2019.
[75] Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, and Hao Yang. Dysat: Deep neural representation learning on dynamic graphs via self-attention networks. In Proceedings of the 13th International Conference on Web Search and Aata Mining, pages 519–527, 2020.
[76] Youngjoo Seo, Michael Defferrard, Pierre Vandergheynst, and Xavier Bresson. Structured sequence modeling with graph convolutional recurrent networks. In Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I 25, pages 362-373. Springer, 2018.
[77] Zheyan Shen, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, and Peng Cui. Towards out-of-distribution generalization: A survey. arXiv preprint arXiv:2108.13624, 2021.
[78] Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, and Philip S. Yu. Graph structure learning with variational information bottleneck. In Thirty-Sixth AAAI Conference on Artificial Intelligence, pages 4165-4174. AAAI Press, 2022.
[79] Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Yuanxing Ning, Philip S Yu, and Lifang He. SUGAR: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism. In Proceedings of the ACM Web Conference, pages 2081-2091, 2021.