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Rethinking spatial community detection in human mobility: A random walk-based method
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DOI：10.1016/j.cities.2025.106674.png)
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Abstract
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• Introduces a random walk-based spatial community detection framework for human mobility networks. • Proposes an optimization algorithm incorporating local best-first strategy and tabu search for spatial community detection. • Synthetic experiments and real-world case studies consistently demonstrate the superiority of the proposed method over topology-based ones. • The communities detected from the Shenzhen shared-bike data mainly reflect four distinct patterns of human mobility.
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