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Identification of communities in urban mobility networks using multi-layer graphs of network traffic
Institution:1. College of Intelligence and Computing, Tianjin University, Tianjin 300350, China;2. Qingdao Huanghai University, Shandong 250000, China;3. School of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China;1. Polytechnic School, Catholic University of Murcia, Murcia, Spain;2. Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente, Ap. Correos 99, E03080 Alicante, Spain
Abstract:This paper proposes a novel approach to identify the pockets of activity or the community structure in a city network using multi-layer graphs that represent the movement of disparate entities (i.e. private cars, buses and passengers) in the network. First, we process the trip data corresponding to each entity through a Voronoi segmentation procedure which provides a natural null model to compare multiple layers in a real world network. Second, given nodes that represent Voronoi cells and link weights that define the strength of connection between them, we apply a community detection algorithm and partition the network into smaller areas independently at each layer. The partitioning algorithm returns geographically well connected regions in all layers and reveal significant characteristics underlying the spatial structure of our city. Third, we test an algorithm that reveals the unified community structure of multi-layer networks, which are combinations of single-layer networks coupled through links between each node in one network layer to itself in other layers. This approach allows us to directly compare the resulting communities in multiple layers where connection types are categorically different.
Keywords:Community detection  Travel patterns  Voronoi  City structure  Mobility graphs
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