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This study presents a new method using the Laplacian matrix for community detection in mobility graphs, which can directly identify community structures without additional processing. This method is particularly advantageous for real-world applications, such as analyzing mobility patterns, as it can impose a k-community structure on graphs irrespective of their quality. Overall, this research provides a Laplacian-constrained framework to explore community structures in mobility graphs, offering insights into the spatial organization of transportation services and the underlying purposes of travel within urban environments.

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Auto-weighted Multi-view Learning for Community Detection: A Laplacian-Constrained Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Yuting Wang,
  • Di Wang

摘要

This study presents a new method using the Laplacian matrix for community detection in mobility graphs, which can directly identify community structures without additional processing. This method is particularly advantageous for real-world applications, such as analyzing mobility patterns, as it can impose a k-community structure on graphs irrespective of their quality. Overall, this research provides a Laplacian-constrained framework to explore community structures in mobility graphs, offering insights into the spatial organization of transportation services and the underlying purposes of travel within urban environments.