{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T01:24:42Z","timestamp":1784510682683,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4,13]]},"DOI":"10.1145\/3774904.3792090","type":"proceedings-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T21:54:34Z","timestamp":1775771674000},"page":"463-474","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Riemannian Liquid Spatio-Temporal Graph Network"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2839-3901","authenticated-orcid":false,"given":"Liangsi","family":"Lu","sequence":"first","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0099-539X","authenticated-orcid":false,"given":"Jingchao","family":"Wang","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5479-2580","authenticated-orcid":false,"given":"Zhaorong","family":"Dai","sequence":"additional","affiliation":[{"name":"South China Agricultural University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7666-192X","authenticated-orcid":false,"given":"Hanqian","family":"Liu","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University, Zhuhai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3928-7495","authenticated-orcid":false,"given":"Yang","family":"Shi","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,12]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International conference on machine learning. PMLR, 486-496","author":"Bachmann Gregor","year":"2020","unstructured":"Gregor Bachmann, Gary B\u00e9cigneul, and Octavian Ganea. 2020. Constant curvature graph convolutional networks. In International conference on machine learning. PMLR, 486-496."},{"key":"e_1_3_2_1_2_1","volume-title":"Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems","author":"Bai Lei","year":"2020","unstructured":"Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems, Vol. 33 (2020), 17804-17815."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583455"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1800683115"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Dmitri Burago Yuri Burago Sergei Ivanov et al. 2001. A course in metric geometry. Vol. 33. American Mathematical Society Providence.","DOI":"10.1090\/gsm\/033"},{"key":"e_1_3_2_1_6_1","volume-title":"Hyperbolic graph convolutional neural networks. Advances in neural information processing systems","author":"Chami Ines","year":"2019","unstructured":"Ines Chami, Zhitao Ying, Christopher R\u00e9, and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_7_1","volume-title":"Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems","author":"Chen Ricky T. Q.","year":"2018","unstructured":"Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. 2018. Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems (2018)."},{"key":"e_1_3_2_1_8_1","unstructured":"Jeongwhan Choi Hwangyong Choi Jeehyun Hwang and Noseong Park. 2022. Graph Neural Controlled Differential Equations for Traffic Forecasting. In AAAI."},{"key":"e_1_3_2_1_9_1","volume-title":"The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ayPPc0SyLv1","author":"Cong Weilin","year":"2023","unstructured":"Weilin Cong, Si Zhang, Jian Kang, Baichuan Yuan, Hao Wu, Xin Zhou, Hanghang Tong, and Mehrdad Mahdavi. 2023. Do We Really Need Complicated Model Architectures For Temporal Networks?. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ayPPc0SyLv1"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467430"},{"key":"e_1_3_2_1_11_1","volume-title":"Approximation of dynamical systems by continuous time recurrent neural networks. Neural networks","author":"Yuichi Nakamura Funahashi","year":"1993","unstructured":"Ken-ichi Funahashi and Yuichi Nakamura. 1993. Approximation of dynamical systems by continuous time recurrent neural networks. Neural networks, Vol. 6, 6 (1993), 801-806."},{"key":"e_1_3_2_1_12_1","volume-title":"Hyperbolic neural networks. Advances in neural information processing systems","author":"Ganea Octavian","year":"2018","unstructured":"Octavian Ganea, Gary B\u00e9cigneul, and Thomas Hofmann. 2018. Hyperbolic neural networks. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the 41st International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"16225","author":"Gravina Alessio","year":"2024","unstructured":"Alessio Gravina, Giulio Lovisotto, Claudio Gallicchio, Davide Bacciu, and Claas Grohnfeldt. 2024a. Long Range Propagation on Continuous-Time Dynamic Graphs. In Proceedings of the 41st International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 235), Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp (Eds.). PMLR, 16206-16225. https:\/\/proceedings.mlr.press\/v235\/gravina24a.html"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2024\/445"},{"key":"e_1_3_2_1_15_1","volume-title":"International conference on learning representations.","author":"Gu Albert","year":"2018","unstructured":"Albert Gu, Frederic Sala, Beliz Gunel, and Christopher R\u00e9. 2018. Learning mixed-curvature representations in product spaces. In International conference on learning representations."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_1_17_1","volume-title":"Structure-preserving algorithms for ordinary differential equations. Geometric numerical integration","author":"Hairer Ernst","year":"2006","unstructured":"Ernst Hairer, Christian Lubich, and Gerhard Wanner. 2006. Structure-preserving algorithms for ordinary differential equations. Geometric numerical integration, Vol. 31 (2006)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00556-7"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16936"},{"key":"e_1_3_2_1_20_1","volume-title":"Liquid Structural State-Space Models. In The Eleventh International Conference on Learning Representations.","author":"Hasani Ramin","unstructured":"Ramin Hasani, Mathias Lechner, Tsun-Hsuan Wang, Makram Chahine, Alexander Amini, and Daniela Rus. [n.d.]. Liquid Structural State-Space Models. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_21_1","volume-title":"Multilayer feedforward networks are universal approximators. Neural networks","author":"Hornik Kurt","year":"1989","unstructured":"Kurt Hornik, Maxwell Stinchcombe, and Halbert White. 1989. Multilayer feedforward networks are universal approximators. Neural networks, Vol. 2, 5 (1989), 359-366."},{"key":"e_1_3_2_1_22_1","volume-title":"Neural controlled differential equations for irregular time series. Advances in neural information processing systems","author":"Kidger Patrick","year":"2020","unstructured":"Patrick Kidger, James Morrill, James Foster, and Terry Lyons. 2020. Neural controlled differential equations for irregular time series. Advances in neural information processing systems, Vol. 33 (2020), 6696-6707."},{"key":"e_1_3_2_1_23_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_24_1","first-page":"1","article-title":"Universal approximation theorems for differentiable geometric deep learning","volume":"23","author":"Kratsios Anastasis","year":"2022","unstructured":"Anastasis Kratsios and L\u00e9onie Papon. 2022. Universal approximation theorems for differentiable geometric deep learning. Journal of Machine Learning Research, Vol. 23, 196 (2022), 1-73.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_25_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=JGO8CvG5S9","author":"Kratsios Anastasis","year":"2022","unstructured":"Anastasis Kratsios, Behnoosh Zamanlooy, Tianlin Liu, and Ivan Dokmani\u0107. 2022. Universal Approximation Under Constraints is Possible with Transformers. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=JGO8CvG5S9"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"e_1_3_2_1_27_1","volume-title":"Simulating hamiltonian dynamics. Number 14","author":"Leimkuhler Benedict","unstructured":"Benedict Leimkuhler and Sebastian Reich. 2004. Simulating hamiltonian dynamics. Number 14. Cambridge university press."},{"key":"e_1_3_2_1_28_1","volume-title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations (ICLR '18)","author":"Li Yaguang","year":"2018","unstructured":"Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations (ICLR '18)."},{"key":"e_1_3_2_1_29_1","volume-title":"Liquid-Graph Time-Constant Network for Multi-Agent Systems Control. In Conference on decision and control","author":"Marino Antonio","year":"2024","unstructured":"Antonio Marino, Claudio Pacchierotti, and Paolo Robuffo Giordano. 2024. Liquid-Graph Time-Constant Network for Multi-Agent Systems Control. In Conference on decision and control 2024."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP46576.2022.9897522"},{"key":"e_1_3_2_1_31_1","volume-title":"Poincar\u00e9 embeddings for learning hierarchical representations. Advances in neural information processing systems","author":"Nickel Maximillian","year":"2017","unstructured":"Maximillian Nickel and Douwe Kiela. 2017. Poincar\u00e9 embeddings for learning hierarchical representations. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_32_1","volume-title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs. In ICML 2020 Workshop on Graph Representation Learning.","author":"Rossi Emanuele","year":"2020","unstructured":"Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. 2020. Temporal Graph Networks for Deep Learning on Dynamic Graphs. In ICML 2020 Workshop on Graph Representation Learning."},{"key":"e_1_3_2_1_33_1","volume-title":"Ricky TQ Chen, and David K Duvenaud","author":"Rubanova Yulia","year":"2019","unstructured":"Yulia Rubanova, Ricky TQ Chen, and David K Duvenaud. 2019. Latent ordinary differential equations for irregularly-sampled time series. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_34_1","volume-title":"International symposium on graph drawing. Springer, 355-366","author":"Sarkar Rik","year":"2011","unstructured":"Rik Sarkar. 2011. Low distortion delaunay embedding of trees in hyperbolic plane. In International symposium on graph drawing. Springer, 355-366."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1137\/080732936"},{"key":"e_1_3_2_1_36_1","volume-title":"Freedyg: Frequency enhanced continuous-time dynamic graph model for link prediction. In The twelfth international conference on learning representations.","author":"Tian Yuxing","year":"2024","unstructured":"Yuxing Tian, Yiyan Qi, and Fan Guo. 2024. Freedyg: Frequency enhanced continuous-time dynamic graph model for link prediction. In The twelfth international conference on learning representations."},{"key":"e_1_3_2_1_37_1","volume-title":"International conference on learning representations.","author":"Trivedi Rakshit","year":"2019","unstructured":"Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, and Hongyuan Zha. 2019. Dyrep: Learning representations over dynamic graphs. In International conference on learning representations."},{"key":"e_1_3_2_1_38_1","volume-title":"Forty-second International Conference on Machine Learning.","author":"Wan Guancheng","year":"2025","unstructured":"Guancheng Wan, Zijie Huang, Wanjia Zhao, Xiao Luo, Yizhou Sun, and Wei Wang. 2025. Rethink graphode generalization within coupled dynamical system. In Forty-second International Conference on Machine Learning."},{"key":"e_1_3_2_1_39_1","volume-title":"Tcl: Transformer-based dynamic graph modelling via contrastive learning. arXiv preprint arXiv:2105.07944","author":"Wang Lu","year":"2021","unstructured":"Lu Wang, Xiaofu Chang, Shuang Li, Yunfei Chu, Hui Li, Wei Zhang, Xiaofeng He, Le Song, Jingren Zhou, and Hongxia Yang. 2021. Tcl: Transformer-based dynamic graph modelling via contrastive learning. arXiv preprint arXiv:2105.07944 (2021)."},{"key":"e_1_3_2_1_40_1","unstructured":"Da Xu Chuanwei Ruan Evren Korpeoglu Sushant Kumar and Kannan Achan. 2020. Inductive Representation Learning on Temporal Graphs. arXiv:2002.07962 [cs.LG] https:\/\/arxiv.org\/abs\/2002.07962"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467422"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2960"},{"key":"e_1_3_2_1_43_1","first-page":"1234","article-title":"GMAN","author":"Zheng Chuanpan","year":"2020","unstructured":"Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. 2020. GMAN: A Graph Multi-Attention Network for Traffic Prediction. In AAAI. 1234-1241.","journal-title":"A Graph Multi-Attention Network for Traffic Prediction. In AAAI."}],"event":{"name":"WWW '26: The ACM Web Conference 2026","location":"Dubai United Arab Emirates","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2026"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774904.3792090","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T07:20:06Z","timestamp":1783149606000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774904.3792090"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,12]]},"references-count":43,"alternative-id":["10.1145\/3774904.3792090","10.1145\/3774904"],"URL":"https:\/\/doi.org\/10.1145\/3774904.3792090","relation":{},"subject":[],"published":{"date-parts":[[2026,4,12]]},"assertion":[{"value":"2026-04-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}