Identification of communities in urban mobility networks using multi-layer graphs of network traffic
Identification of communities in urban mobility networks using multi-layer graphs of network traffic
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DOI：
10.1016/j.trc.2018.02.015
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发表时间：
2018-04-01
影响因子：
8.3
通讯作者：
Kim, Jiwon
中科院分区：
文献类型：
--
作者：
Yildirimoglu, Mehmet;Kim, Jiwon
本文提出了一种新的方法来识别口袋的活动或社区结构在城市网络中使用多层图，表示不同的实体（即私人汽车，公共汽车和乘客）在网络中的运动。首先，我们通过Voronoi分割过程处理对应于每个实体的行程数据，该过程提供了一个自然的空模型来比较真实的世界网络中的多个层。其次，给定代表Voronoi单元的节点和定义它们之间连接强度的链接权重，我们应用社区检测算法并在每一层独立地将网络划分为较小的区域。分区算法在所有层中返回地理上连接良好的区域，并揭示了我们城市空间结构的重要特征。第三，我们测试一个算法，揭示了统一的社区结构的多层网络，这是单层网络的组合，通过每个节点之间的链接耦合在一个网络层到其他层。这种方法允许我们直接比较多个层中的结果社区，其中连接类型是完全不同的。
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.