Volume 3, Issue 3
Spatial Correlation Function in Modular Networks

Ke Ping Li & Zi You Gao

DOI:

Commun. Comput. Phys., 3 (2008), pp. 724-733.

Published online: 2008-03

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  • Abstract

Due to the complexity of the interactions among the nodes of the complex networks, the properties of the network modules, to a large extent, remain unknown or unexplored. In this paper, we introduce the spatial correlation function Grs to describe the correlations among the modules of the weighted networks. In order to test the proposed method, we use our method to analyze and discuss the modular structures of the ER random networks, scale-free networks and the Chinese railway network. Rigorous analysis of the existing data shows that the spatial correlation function Grs is suitable for describing the correlations among different network modules. Remarkably, we find that different networks display different correlations, especially, the correlation function Grs with different networks meets different degree distribution, such as the linear and exponential distributions.

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@Article{CiCP-3-724, author = {}, title = {Spatial Correlation Function in Modular Networks}, journal = {Communications in Computational Physics}, year = {2008}, volume = {3}, number = {3}, pages = {724--733}, abstract = {

Due to the complexity of the interactions among the nodes of the complex networks, the properties of the network modules, to a large extent, remain unknown or unexplored. In this paper, we introduce the spatial correlation function Grs to describe the correlations among the modules of the weighted networks. In order to test the proposed method, we use our method to analyze and discuss the modular structures of the ER random networks, scale-free networks and the Chinese railway network. Rigorous analysis of the existing data shows that the spatial correlation function Grs is suitable for describing the correlations among different network modules. Remarkably, we find that different networks display different correlations, especially, the correlation function Grs with different networks meets different degree distribution, such as the linear and exponential distributions.

}, issn = {1991-7120}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/cicp/7872.html} }
TY - JOUR T1 - Spatial Correlation Function in Modular Networks JO - Communications in Computational Physics VL - 3 SP - 724 EP - 733 PY - 2008 DA - 2008/03 SN - 3 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/cicp/7872.html KW - AB -

Due to the complexity of the interactions among the nodes of the complex networks, the properties of the network modules, to a large extent, remain unknown or unexplored. In this paper, we introduce the spatial correlation function Grs to describe the correlations among the modules of the weighted networks. In order to test the proposed method, we use our method to analyze and discuss the modular structures of the ER random networks, scale-free networks and the Chinese railway network. Rigorous analysis of the existing data shows that the spatial correlation function Grs is suitable for describing the correlations among different network modules. Remarkably, we find that different networks display different correlations, especially, the correlation function Grs with different networks meets different degree distribution, such as the linear and exponential distributions.

Ke Ping Li & Zi You Gao. (2020). Spatial Correlation Function in Modular Networks. Communications in Computational Physics. 3 (3). 724-733. doi:
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