[40] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, 2015.
[41] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
[42] Thomas N Kipf and Max Welling. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308, 2016.
[43] Alexander Kraskov, Harald Stögbauer, and Peter Grassberger. Estimating mutual information. Physical Review E, 69(6):066138, 2004.
[44] David Krueger, Ethan Caballero, Jorn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, and Aaron C. Courville. Out-of-distribution generalization via risk extrapolation (rex). In Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 5815-5826. PMLR, 2021.
[45] 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 and Data Mining, pages 1269-1278. ACM, 2019.
[46] Bo Li, Yezhen Wang, Shanghang Zhang, Dongsheng Li, Kurt Keutzer, Trevor Darrell, and Han Zhao. Learning invariant representations and risks for semi-supervised domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1104-1113, 2021.
[47] Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Fan Yang, Funing Sun, Depeng Jin, and Yong Li. Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution. ACM Transactions on Knowledge Discovery from Data, 17(1):1-21, 2023.
[48] Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, and Wenwu Zhu. Disentangled contrastive learning on graphs. Advances in Neural Information Processing Systems, 34:21872-21884, 2021.
[49] Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu. OOD-GNN: Out-of-distribution generalized graph neural network. IEEE Transactions on Knowledge and Data Engineering, 2022.
[50] Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu. Out-of-distribution generalization on graphs: A survey. arXiv preprint arXiv:2202.07987, 2022.
[51] Haoyang Li, Ziwei Zhang, Xin Wang, and Wenwu Zhu. Disentangled graph contrastive learning with independence promotion. IEEE Transactions on Knowledge and Data Engineering, 2022.
[52] Haoyang Li, Ziwei Zhang, Xin Wang, and Wenwu Zhu. Learning invariant graph representations for out-of-distribution generalization. In Advances in Neural Information Processing Systems, 2022.
[53] Jianxin Li, Qingyun Sun, Hao Peng, Beining Yang, Jia Wu, and S Yu Phillip. Adaptive subgraph neural network with reinforced critical structure mining. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
[54] Jianxin Li, Shuai Zhang, Hui Xiong, and Haoyi Zhou. Autost: Towards the universal modeling of spatio-temporal sequences. In Advances in Neural Information Processing Systems, volume 35, pages 20498-20510, 2022.
[55] Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Ratsch, Sylvain Gelly, Bernhard Scholkopf, and Olivier Bachem. Challenging common assumptions in the unsupervised learning of disentangled representations. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 4114-4124. PMLR, 2019.
[56] Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. Disentangled graph convolutional networks. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 4212-4221. PMLR, 2019.
[57] Franco Manessi, Alessandro Rozza, and Mario Manzo. Dynamic graph convolutional networks. Pattern Recognition, 97:107000, 2020.
[58] Larry R Medsker and LC Jain. Recurrent neural networks. Design and Applications, 5:64-67, 2001.
[59] Tomás Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. In 1st International Conference on Learning Representations, 2013.