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Evolutionary link community structure discovery in dynamic weighted networks

Author

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  • Liu, Qiang
  • Liu, Caihong
  • Wang, Jiajia
  • Wang, Xiang
  • Zhou, Bin
  • Zou, Peng

Abstract

Traditional community detection methods are often restricted in static network analysis. In fact, most of networks in real world obviously show dynamic characteristics with time passing. In this paper, we design a link community structure discovery algorithm in dynamic weighted networks, which can not only reveal the evolutionary link community structure, but also detect overlapping communities by mapping link communities to node communities. Meanwhile, our algorithm can also get the hierarchical structure of link communities by tuning a parameter. The proposed algorithm is based on weighted edge fitness and weighted partition density so as to determine whether to add a link to a community and whether to merge two communities to form a new link community. Experiments on both synthetic and real world networks demonstrate the proposed algorithm can detect evolutionary link community structure in dynamic weighted networks effectively.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:466:y:2017:i:c:p:370-388
    DOI: 10.1016/j.physa.2016.09.028
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    References listed on IDEAS

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    1. 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.
    2. Zhou, Xu & Liu, Yanheng & Li, Bin & Sun, Geng, 2015. "Multiobjective biogeography based optimization algorithm with decomposition for community detection in dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 430-442.
    3. Andrea Lancichinetti & Filippo Radicchi & José J Ramasco & Santo Fortunato, 2011. "Finding Statistically Significant Communities in Networks," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-18, April.
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    Cited by:

    1. 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.
    2. Zhan, Weihua & Deng, Lei & Guan, Jihong & Niu, Jun & Sun, Dechao, 2020. "Revealing dynamic communities in networks using genetic algorithm with merge and split operators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).

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