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Spatial-temporal graph models are prevailing for abstracting and modelling spatial and temporal dependencies.
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Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018 · 2018
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Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden , Jérôme Lang (Ed.). ijcai.org, 3634–3640
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Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of the Ethereum Graph?. In Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020, Cincinnati, Ohio, USA, May 7-9, 2020 , Carlotta Demeniconi and Nitesh V. Chawla (Eds.). SIAM, 523–531
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TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022
Yuzhou Chen, Ignacio Segovia-Dominguez, Baris Coskunuzer, and Yulia R. Gel. 2022 · 2022
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Graph Neural Controlled Differential Equations for Traffic Forecasting. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022 . AAAI Press, 6367–6374
Jeongwhan Choi, Hwangyong Choi, Jeehyun Hwang, and Noseong Park. 2022 · 2022
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Adaptive spatial-temporal graph attention networks for traffic flow forecasting
Xiangyuan Kong, Jian Zhang, Xiang Wei, Weiwei Xing, and Wei Lu. 2022 · 2022
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Combating Distribution Shift for Accurate Time Series Forecasting via Hypernetworks. In 28th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2022, Nanjing, China, January 10-12, 2023 . IEEE, 900–907
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