中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey

文献类型：期刊论文

作者Jin, Guangyin1,2; Liang, Yuxuan3; Fang, Yuchen4; Shao, Zezhi5; Huang, Jincai6; Zhang, Junbo7; Zheng, Yu7
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2024-10-01
卷号36期号:10页码:5388-5408
关键词Surveys Task analysis Time series analysis Transportation Topology Graph neural networks Deep learning predictive learning spatio-temporal data mining time series urban computing
ISSN号1041-4347
DOI10.1109/TKDE.2023.3333824
英文摘要With recent advances in sensing technologies, a myriad of spatio-temporal data has been generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal data is an important yet demanding aspect of urban computing, which can enhance intelligent management decisions in various fields, including transportation, environment, climate, public safety, healthcare, and others. Traditional statistical and deep learning methods struggle to capture complex correlations in urban spatio-temporal data. To this end, Spatio-Temporal Graph Neural Networks (STGNN) have been proposed, achieving great promise in recent years. STGNNs enable the extraction of complex spatio-temporal dependencies by integrating graph neural networks (GNNs) and various temporal learning methods. In this manuscript, we provide a comprehensive survey on recent progress on STGNN technologies for predictive learning in urban computing. Firstly, we provide a brief introduction to the construction methods of spatio-temporal graph data and the prevalent deep-learning architectures used in STGNNs. We then sort out the primary application domains and specific predictive learning tasks based on existing literature. Afterward, we scrutinize the design of STGNNs and their combination with some advanced technologies in recent years. Finally, we conclude the limitations of existing research and suggest potential directions for future work.
资助项目Guangzhou Municiple Science and Technology Project[2023A03J0011] ; National Natural Science Foundation of China[62172034] ; National Natural Science Foundation of China[72242106] ; Beijing Natural Science Foundation[4212021]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001313362200027
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/39575]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Liang, Yuxuan
作者单位1.Natl Innovat Inst Def Technol, Beijing 100071, Peoples R China
2.Natl Univ Def Technol, Changsha 410003, Peoples R China
3.Hong Kong Univ Sci & Technol Guangzhou, Intelligent Transportat Thrust, Guangzhou 511442, Peoples R China
4.Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611730, Peoples R China
5.Univ Chinese Acad Sci, Inst Comp Technol, Beijing 100086, Peoples R China
6.Natl Univ Def Technol, Coll Syst Engn, Changsha 410003, Peoples R China
7.JD Technol, JD Intelligent Cities Res & JD iCity, Beijing 100176, Peoples R China
推荐引用方式
GB/T 7714		 Jin, Guangyin,Liang, Yuxuan,Fang, Yuchen,et al. Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2024,36(10):5388-5408.
APA Jin, Guangyin.,Liang, Yuxuan.,Fang, Yuchen.,Shao, Zezhi.,Huang, Jincai.,...&Zheng, Yu.(2024).Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,36(10),5388-5408.
MLA Jin, Guangyin,et al."Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 36.10(2024):5388-5408.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。