Smart City Gnosys

Smart city article details

Title	Unified Spatio-Temporal Graph Neural Networks: Data-Driven Modeling For Social Science
ID_Doc	59603
Authors	Li Y.; Lin Y.; Gao Y.; Khan L.
Year	2022
Published	Proceedings of the International Joint Conference on Neural Networks, 2022-July
DOI	http://dx.doi.org/10.1109/IJCNN55064.2022.9892911
Abstract	Time series forecasting with additional spatial de-pendencies has attracted a tremendous amount of research interest in social sciences, due to its importance in modern real-world applications. The Graph Neural Networks (GNN) is one of the most exciting deep learning techniques among these spatio-temporal modeling approaches. Most existing spatio-temporal GNN frameworks are based on a two-step modeling process. In such scenario, spatial and temporal dependencies are modeled in separate steps, which lead to problems such as complex architecture design, hard to scale, etc. Targeting the shortcomings of existing studies, we take both spatial and temporal dependencies from another perspective, and consider them as two heterogeneous types of edges in the graph. We propose a unified spatio-temporal GNN framework that captures both dependencies in a single step. More specifically, for each node in the graph, a unified neural network component is designed to simultaneously extract information from its sur-rounding neighbors (spatial) and its past records (temporal), which enables much easier dependency aggregation with faster execution. Experiment results demonstrate the superiority of the proposed framework over state-of-the-art (SOTA) baselines on various applications, including modeling smart cities and data-driven political science research. © 2022 IEEE.
Author Keywords	graph neural networks; spatio-temporal modeling

Similar Articles

Id	Similarity	Authors	Title	Published
52567	0.927	Jin G.; Liang Y.; Fang Y.; Shao Z.; Huang J.; Zhang J.; Zheng Y.	Spatio-Temporal Graph Neural Networks For Predictive Learning In Urban Computing: A Survey	IEEE Transactions on Knowledge and Data Engineering, 36, 10 (2024)
20803	0.919	Wang S.; Chen Y.; Xu H.; Hu J.; Zeng P.; Hu Z.	Dngnn: Efficient Deep Noisy Graph Neural Network For Spatio-Temporal Series Forecasting	2025 2nd International Conference on Algorithms, Software Engineering and Network Security, ASENS 2025 (2025)
52572	0.894	Tang J.; Qian T.; Liu S.; Du S.; Hu J.; Li T.	Spatio-Temporal Latent Graph Structure Learning For Traffic Forecasting	Proceedings of the International Joint Conference on Neural Networks, 2022-July (2022)
52568	0.892	Ren H.; Kang J.; Zhang K.	Spatio-Temporal Graph-Tcn Neural Network For Traffic Flow Prediction	2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2022 (2022)
37180	0.891	Liang Y.; Ouyang K.; Wang Y.; Pan Z.; Yin Y.; Chen H.; Zhang J.; Zheng Y.; Rosenblum D.S.; Zimmermann R.	Mixed-Order Relation-Aware Recurrent Neural Networks For Spatio-Temporal Forecasting	IEEE Transactions on Knowledge and Data Engineering, 35, 9 (2023)
52516	0.891	Dao M.-S.; Zetsu K.; Hoang D.-T.	Spatial-Temporal Graph Transformer Network For Spatial-Temporal Forecasting	Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024 (2024)
38415	0.886	Zhao W.; Zhang S.; Zhou B.; Wang B.	Multi-Spatio-Temporal Fusion Graph Recurrent Network For Traffic Forecasting	Engineering Applications of Artificial Intelligence, 124 (2023)
60280	0.885	Li Z.; Xia L.; Tang J.; Xu Y.; Shi L.; Xia L.; Yin D.; Huang C.	Urbangpt: Spatio-Temporal Large Language Models	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2024)
6155	0.882	Jin X.-B.; Ma H.; Xie J.-Y.; Kong J.; Deveci M.; Kadry S.	Ada-Stgmat: An Adaptive Spatio-Temporal Graph Multi-Attention Network For Intelligent Time Series Forecasting In Smart Cities	Expert Systems with Applications, 269 (2025)
23809	0.88	Ouyang X.; Li Y.; Guo D.; Huang W.; Yang X.; Yang Y.; Zhang J.; Li T.	Enhancing Few-Sample Spatio-Temporal Prediction Via Relational Fusion-Based Hypergraph Neural Network	Information Fusion, 121 (2025)