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Evolutionary community structure discovery in dynamic weighted networks

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  • Guo, Chonghui
  • Wang, Jiajia
  • Zhang, Zhen

Abstract

Detecting evolutionary community structure in dynamic weighted networks is important for understanding the structure and functions of networks. In this paper, an algorithm which considers the historic community structure of networks is developed to detect evolutionary community structure in dynamic weighted networks. In the proposed algorithm, two measures are proposed to determine whether to add a node to a community and whether to merge two communities to form a new community. The proposed algorithm can automatically discover evolutionary community structure in weighted networks whose number of nodes and communities is changing over time and does not need to determine the number of communities in advance. The algorithm is tested using a synthetic network and two real-word complex networks. Experimental results demonstrate that the proposed algorithm can discover evolutionary community structure in dynamic weighted networks effectively.

Suggested Citation

  • Guo, Chonghui & Wang, Jiajia & Zhang, Zhen, 2014. "Evolutionary community structure discovery in dynamic weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 565-576.
    • Handle: RePEc:eee:phsmap:v:413:y:2014:i:c:p:565-576
    DOI: 10.1016/j.physa.2014.07.004
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    References listed on IDEAS

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    1. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    2. Chen, Duanbing & Shang, Mingsheng & Lv, Zehua & Fu, Yan, 2010. "Detecting overlapping communities of weighted networks via a local algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(19), pages 4177-4187.
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    Cited by:

    1. Jordan Cambe & Sebastian Grauwin & Patrick Flandrin & Pablo Jensen, 2022. "A new clustering method to explore the dynamics of research communities," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4459-4482, August.
    2. Liu, Qiang & Liu, Caihong & Wang, Jiajia & Wang, Xiang & Zhou, Bin & Zou, Peng, 2017. "Evolutionary link community structure discovery in dynamic weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 370-388.
    3. Guo, Yajuan & Yang, Licai & Hao, Shenxue & Gao, Jun, 2019. "Dynamic identification of urban traffic congestion warning communities in heterogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 98-111.
    4. Xin, Yu & Xie, Zhi-Qiang & Yang, Jing, 2016. "The adaptive dynamic community detection algorithm based on the non-homogeneous random walking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 241-252.

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