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Dynamic meta-graph convolutional recurrent network for heterogeneous spatiotemporal graph forecasting.

Xianwei Guo1, Zhiyong Yu1, Fangwan Huang1

  • 1College of Computer and Data Science, Fuzhou University, WuLong Jiang North Avenue, University Town, Fuzhou, 350108, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou University, WuLong Jiang North Avenue, University Town, Fuzhou, 350108, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 25, 2024
PubMed
Summary

This study introduces a new Dynamic Meta-Graph Convolutional Recurrent Network (DMetaGCRN) for Spatiotemporal Graph (STG) forecasting. The framework addresses dynamic spatial dependencies and data heterogeneity in urban computing, outperforming existing methods.

Keywords:
Dynamic graph generationHeterogeneityMeta-graphSpatiotemporal graph forecasting

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Area of Science:

  • Spatiotemporal data mining
  • Urban computing
  • Graph neural networks

Background:

  • Spatiotemporal Graph (STG) forecasting is crucial for urban computing.
  • Existing Spatiotemporal Graph Neural Networks (STGNNs) struggle with dynamic spatial dependencies and data heterogeneity in urban networks.

Purpose of the Study:

  • Propose a novel framework, Dynamic Meta-Graph Convolutional Recurrent Network (DMetaGCRN), for Spatiotemporal Graph learning.
  • Address limitations in current STGNNs regarding dynamic spatial dependencies and urban data heterogeneity.

Main Methods:

  • Developed a meta-graph generator to dynamically create graph structures using sensor signals, historical trends, periodic information, and meta-node embeddings.
  • Utilized a memory network to guide meta-node embedding learning.
  • Designed a Dynamic Meta-Graph Convolutional Recurrent Unit (DMetaGCRU) for simultaneous spatial and temporal dependency modeling.
  • Implemented DMetaGCRN using an encoder-decoder architecture.

Main Results:

  • The meta-graph generation process effectively simulates dynamic spatial dependencies and captures data heterogeneity.
  • DMetaGCRN demonstrated superior performance compared to state-of-the-art approaches on four real-world urban spatiotemporal datasets.

Conclusions:

  • The proposed DMetaGCRN framework offers a significant advancement in Spatiotemporal Graph forecasting.
  • DMetaGCRN effectively handles dynamic spatial dependencies and data heterogeneity, improving urban computing applications.