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Attribute prediction of spatio-temporal graph nodes based on weighted graph diffusion convolution network

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Abstract

Spatio-temporal graph data can be analyzed by effectively mining for realizing spatio-temporal graph data prediction. It is of great significance to predict spatio-temporal graph attribute features of future moments under different scenarios. However, the current research represent the spatio-temporal graph as a fixed graph structure in the learning process and cannot describe real dependencies in spatial feature learning, which has great limitations and cannot effectively capture the dynamic dependence of spatio-temporal graph data. Therefore, this paper proposes a Deep Spatio-Temporal Graph Node Attribute Prediction Model Based on Weighted Graph Diffusion Convolution Network, named DST-WDCN. Firstly, the graph adjacency matrix is defined based on both the explicit graph structure and the hidden semantic relationships. Secondly, a dynamic weighting learning method is introduced to dynamically weight the graph structure, which is incorporated into the gated diffusion convolution for dynamic spatial correlation extraction. Thirdly, a gated dilated causal convolution is proposed to extract temporal features to fully model temporal nonlinear dependencies. Finally, the spatio-temporal convolution blocks are stacked to effectively capture the spatio-temporal dynamic correlation and realize the attribute prediction of the spatio-temporal graph in the future time. Extensive experiments over three real-world data sets have shown our proposed model is superior to the benchmark model in terms of MAE, RMSE and MAPE. The effectiveness of each component of the model has been verified through ablation experiments, and the adaptability of the dynamic weighting method has been demonstrated through learning cases.

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Availability of data and materials

The METR-LA and PEMS-BAY datasets released by Li(https://github.com/liyaguang/DCRNN). The JINAN dataset released by Guo(https://github.com/guokan987/HGCN)that obtained from DiDi(https://gaia.didichuxing.com).The AIR_BJ dataset released by Zheng(http://research.microsoft.com/apps/pubs/?id=246398).The HYSK datasets is protected by privacy

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Funding

This study was funded by the National Key Research and Development Program of China (No.2022YFC3004603); National Natural Science Foundation of China (No.62072220); Natural Science Foundation of Liaoning Province (2022-KF-13-06)

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Authors and Affiliations

  1. School of Information, Liaoning University, Shenyang Liaoning, 110036, China

    Linlin Ding, Haiyou Yu, Chenli Zhu & Ji Ma

  2. Information Engineering College, Shenyang University, Shenyang Liaoning, 110044, China

    Yue Zhao

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  1. Linlin Ding
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  2. Haiyou Yu
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  4. Ji Ma
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Contributions

Linlin Ding: Analysis, Funding acquisition, Supervision. Haiyou Yu: Writing original draft, Methodology, Software, Validation, Experiment, Investigation. Chenli Zhu: Data support, Review. Ji Ma: Experiment, Software. Yue Zhao: Experiment, Proofreading.

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Correspondence to Ji Ma.

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Ding, L., Yu, H., Zhu, C. et al. Attribute prediction of spatio-temporal graph nodes based on weighted graph diffusion convolution network. World Wide Web 26, 3655–3690 (2023). https://doi.org/10.1007/s11280-023-01198-4

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