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Subgraph-aware graph structure revision for spatial-temporal graph modeling

文献类型：期刊论文

作者Wang, Yuhu2,3; Zhang, Chunxia1; Xiang, Shiming2,3; Pan, Chunhong3
刊名NEURAL NETWORKS
出版日期2022-10-01
卷号154页码:190-202
ISSN号0893-6080
关键词Graph structure learning Graph neural network Spatial-temporal graph modeling
DOI10.1016/j.neunet.2022.07.017
通讯作者Xiang, Shiming(smxiang@nlpr.ia.ac.cn)
英文摘要Spatial-temporal graph modeling has been widely studied in many fields, such as traffic forecasting and energy analysis, where data has time and space properties. Existing methods focus on capturing stable and dynamic spatial correlations by constructing physical and virtual graphs along with graph convolution and temporal modeling. However, existing methods tending to smooth node features may obscure the spatial-temporal patterns among nodes. Worse, the graph structure is not always available in some fields, while the manually constructed stable or dynamic graphs cannot necessarily reflect the true spatial correlations either. This paper proposes a Subgraph-Aware Graph Structure Revision network (SAGSR) to overcome these limitations. Architecturally, a subgraph-aware structure revision graph convolution module (SASR-GCM) is designed, which revises the learned stable graph to obtain a dynamic one to automatically infer the dynamics of spatial correlations. Each of these two graphs is separated into one homophilic subgraph and one heterophilic subgraph by a subgraph-aware graph convolution mechanism, which aggregates similar nodes in the homophilic subgraph with positive weights, while keeping nodes with dissimilar features in the heterophilic subgraph mutually away with negative aggregation weights to avoid pattern obfuscation. By combining a gated multi-scale temporal convolution module (GMS-TCM) for temporal modeling, SAGSR can efficiently capture the spatial-temporal correlations and extract complex spatial-temporal graph features. Extensive experiments, conducted on two specific tasks: traffic flow forecasting and energy consumption forecasting, indicate the effectiveness and superiority of our proposed approach over several competitive baselines. (C) 2022 Elsevier Ltd. All rights reserved.
WOS关键词CONVOLUTIONAL NETWORKS ; TRAFFIC FLOW
资助项目National Key Research and Development Program of China[2020AAA0104903] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[62072039] ; National Natural Science Foundation of China[62076242]
WOS研究方向Computer Science ; Neurosciences & Neurology
语种英语
出版者PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号WOS:000888516900009
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/50782]  
专题自动化研究所_模式识别国家重点实验室_遥感图像处理团队
通讯作者Xiang, Shiming
作者单位1.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Yuhu,Zhang, Chunxia,Xiang, Shiming,et al. Subgraph-aware graph structure revision for spatial-temporal graph modeling[J]. NEURAL NETWORKS,2022,154:190-202.
APA Wang, Yuhu,Zhang, Chunxia,Xiang, Shiming,&Pan, Chunhong.(2022).Subgraph-aware graph structure revision for spatial-temporal graph modeling.NEURAL NETWORKS,154,190-202.
MLA Wang, Yuhu,et al."Subgraph-aware graph structure revision for spatial-temporal graph modeling".NEURAL NETWORKS 154(2022):190-202.

入库方式： OAI收割

来源：自动化研究所

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