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Accuracy and precision of methods for community identification in weighted networks

Author

Listed:
  • Fan, Ying
  • Li, Menghui
  • Zhang, Peng
  • Wu, Jinshan
  • Di, Zengru

Abstract

Different algorithms, which take both links and link weights into account for the community structure of weighted networks, have been reported recently. Based on the measure of similarity among community structures introduced in our previous work, in this paper, accuracy and precision of three algorithms are investigated. Results show that Potts model based algorithm and weighted extremal optimization (WEO) algorithm work well on both dense or sparse weighted networks, while weighted Girvan–Newman (WGN) algorithm works well only for relatively sparse networks.

Suggested Citation

  • Fan, Ying & Li, Menghui & Zhang, Peng & Wu, Jinshan & Di, Zengru, 2007. "Accuracy and precision of methods for community identification in weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 377(1), pages 363-372.
    • Handle: RePEc:eee:phsmap:v:377:y:2007:i:1:p:363-372
    DOI: 10.1016/j.physa.2006.11.036
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    Cited by:

    1. Zhou, Kuang & Martin, Arnaud & Pan, Quan, 2015. "A similarity-based community detection method with multiple prototype representation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 519-531.
    2. Zhang, Dawei & Xie, Fuding & Zhang, Yong & Dong, Fangyan & Hirota, Kaoru, 2010. "Fuzzy analysis of community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(22), pages 5319-5327.

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