[13] Praveen Venkateswaran, Vinod Muthusamy, Vatche Isahagian, and Nalini Venkatasubramanian. Environment agnostic invariant risk minimization for classification of sequential datasets. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 1615-1624, 2021.
[14] Wang Lu, Jindong Wang, Yiqiang Chen, and Xinwei Sun. Diversify to generalize: Learning generalized representations for time series classification. arXiv preprint arXiv:2209.07027, 2021.
[15] Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He, and Tat-Seng Chua. Discovering invariant rationales for graph neural networks. arXiv preprint arXiv:2201.12872, 2022.
[16] Qitian Wu, Hengrui Zhang, Junchi Yan, and David Wipf. Handling distribution shifts on graphs: An invariance perspective. arXiv preprint arXiv:2202.02466, 2022.
[17] Mucong Ding, Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Micah Goldblum, David Wipf, Furong Huang, and Tom Goldstein. A closer look at distribution shifts and out-of-distribution generalization on graphs. In NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications, 2021.
[18] Tian Jin, Qiong Wu, Xuan Ou, and Jianjun Yu. Community detection and co-author recommendation in co-author networks. International Journal of Machine Learning and Cybernetics, 12(2):597-609, 2021.
[19] Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Min Lin, and Tat-Seng Chua. Causal representation learning for out-of-distribution recommendation. In Proceedings of the ACM Web Conference 2022, pages 3562-3571, 2022.
[20] Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. Invariant risk minimization. arXiv preprint arXiv:1907.02893, 2019.
[21] Shiyu Chang, Yang Zhang, Mo Yu, and Tommi Jaakkola. Invariant rationalization. In International Conference on Machine Learning, pages 1448-1458. PMLR, 2020.
[22] Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney, and Amit Dhurandhar. Invariant risk minimization games. In International Conference on Machine Learning, pages 145-155. PMLR, 2020.
[23] 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, pages 2358-2366, 2022.
[24] 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 Data Mining, pages 519–527, 2020.
[25] Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao Schardl, and Charles Leiserson. Evolvegen: Evolving graph convolutional networks for dynamic graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 5363-5370, 2020.
[26] James W Cooley and John W Tukey. An algorithm for the machine calculation of complex fourier series. Mathematics of computation, 19(90):297-301, 1965.
[27] Ronald Newbold Bracewell and Ronald N Bracewell. The Fourier transform and its applications, volume 31999. McGraw-Hill New York, 1986.
[28] Kun Yi, Qi Zhang, Shoujin Wang, Hui He, Guodong Long, and Zhendong Niu. Neural time series analysis with fourier transform: A survey. arXiv preprint arXiv:2302.02173, 2023.
[29] Qinwei Xu, Ruipeng Zhang, Ya Zhang, Yanfeng Wang, and Qi Tian. A fourier-based framework for domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14383-14392, 2021.
[30] Bruce C Hansen and Robert F Hess. Structural sparseness and spatial phase alignment in natural scenes. JOSA A, 24(7):1873-1885, 2007.
[31] Alan V Oppenheim and Jae S Lim. The importance of phase in signals. Proceedings of the IEEE, 69(5):529-541, 1981.
[32] Leon N Piotrowski and Fergus W Campbell. A demonstration of the visual importance and flexibility of spatial-frequency amplitude and phase. Perception, 11(3):337-346, 1982.