Spectral Bisection Community Detection Method for Urban Road Networks

Author(s):  
Lu Guo ◽  
Ying Cui ◽  
Haili Liang ◽  
Zhao Zhou
Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Wen-Long Shang ◽  
Yanyan Chen ◽  
Huibo Bi ◽  
Haoran Zhang ◽  
Changxi Ma ◽  
...  

Urban road networks are typical complex systems, which are crucial to our society and economy. In this study, topological characteristics of a number of urban road networks purely based on physical roads rather than routes of vehicles or buses are investigated in order to discover underlying unique structural features, particularly compared to other types of transport networks. Based on these topological indices, correlations between topological indices and small-worldness of urban road networks are also explored. The finding shows that there is no significant small-worldness for urban road networks, which is apparently different from other transport networks. Following this, community detection of urban road networks is conducted. The results reveal that communities and hierarchy of urban road networks tend to follow a general nature rule.


2021 ◽  
pp. 1-15
Author(s):  
Hong Zhang ◽  
Peichao Gao ◽  
Tian Lan ◽  
Chengliang Liu
Keyword(s):  

Author(s):  
Amine M. Falek ◽  
Antoine Gallais ◽  
Cristel Pelsser ◽  
Sebastien Julien ◽  
Fabrice Theoleyre
Keyword(s):  

2021 ◽  
Vol 565 ◽  
pp. 32-45
Author(s):  
Dongqing Zhang ◽  
Yucheng Dong ◽  
Zhaoxia Guo

Author(s):  
Fuzhong Nian ◽  
Li Luo ◽  
Xuelong Yu ◽  
Xin Guo

The iterative propagation of information between nodes will strengthen the connection strength between nodes, and the network can evolve into different groups according to difference edge strength. Based on this observation, we present the user engagement to quantify the influences of users different propagation modes to network propagation, and construct weight network to simulate real social network, and proposed the community detection method in social networks based on information propagation and user engagement. Our method can produce different scale communities and overlapping community. We also applied our method to real-world social networks. The experiment proved that the network spread and the community division interact with each other. The community structure is significantly different in the network propagation of different scales.


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