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Surveying network community structure in the hidden metric space

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  • Ma, Lili
  • Jiang, Xin
  • Wu, Kaiyuan
  • Zhang, Zhanli
  • Tang, Shaoting
  • Zheng, Zhiming

Abstract

Most real-world networks from various fields share a universal topological property as community structure. In this paper, we propose a node-similarity based mechanism to explore the formation of modular networks by applying the concept of hidden metric spaces of complex networks. It is demonstrated that network community structure could be formed according to node similarity in the underlying hidden metric space. To clarify this, we generate a set of observed networks using a typical kind of hidden metric space model. By detecting and analyzing corresponding communities both in the observed network and the hidden space, we show that the values of the fitness are rather close, and the assignments of nodes for these two kinds of community structures detected based on the fitness parameter are extremely matching ones. Furthermore, our research also shows that networks with strong clustering tend to display prominent community structures with large values of network modularity and fitness.

Suggested Citation

  • Ma, Lili & Jiang, Xin & Wu, Kaiyuan & Zhang, Zhanli & Tang, Shaoting & Zheng, Zhiming, 2012. "Surveying network community structure in the hidden metric space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 371-378.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:1:p:371-378
    DOI: 10.1016/j.physa.2011.07.057
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    References listed on IDEAS

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    1. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    2. Zhang, Zhanli & Jiang, Xin & Ma, Lili & Tang, Shaoting & Zheng, Zhiming, 2011. "Detecting communities in clustered networks based on group action on set," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1171-1181.
    3. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    4. Marián Boguñá & Fragkiskos Papadopoulos & Dmitri Krioukov, 2010. "Sustaining the Internet with hyperbolic mapping," Nature Communications, Nature, vol. 1(1), pages 1-8, December.
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    Cited by:

    1. Wang, Zuxi & Li, Qingguang & Jin, Fengdong & Xiong, Wei & Wu, Yao, 2016. "Hyperbolic mapping of complex networks based on community information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 455(C), pages 104-119.

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