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The detection of community structure in network via an improved spectral method

Author

Listed:
  • Xie, Fuding
  • Ji, Min
  • Zhang, Yong
  • Huang, Dan

Abstract

Many networks of interest in the science, including social networks, computer networks and the World Wide Web, are found to be divided naturally into communities or groups. The problem of detecting communities is one of the outstanding issues in the study of network systems. Based on the improved shared nearest neighbor (SNN) similarity matrix, spectral method and fuzzy c-means (FCM) clustering algorithm, this paper proposes a new algorithm for detecting the communities in complex networks. The experiment reveals the validity of the presented method. The results are compared with other ones obtained by the different existing well methods and the conclusion is that the accuracy of the results calculated by this approach is much better than the known ones.

Suggested Citation

  • Xie, Fuding & Ji, Min & Zhang, Yong & Huang, Dan, 2009. "The detection of community structure in network via an improved spectral method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(15), pages 3268-3272.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:15:p:3268-3272
    DOI: 10.1016/j.physa.2009.04.036
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    Citations

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    Cited by:

    1. Shang, Ronghua & Bai, Jing & Jiao, Licheng & Jin, Chao, 2013. "Community detection based on modularity and an improved genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1215-1231.
    2. Shang, Ronghua & Luo, Shuang & Zhang, Weitong & Stolkin, Rustam & Jiao, Licheng, 2016. "A multiobjective evolutionary algorithm to find community structures based on affinity propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 203-227.
    3. Shang, Ronghua & Zhang, Weitong & Jiao, Licheng & Stolkin, Rustam & Xue, Yu, 2017. "A community integration strategy based on an improved modularity density increment for large-scale networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 471-485.
    4. Wu, Jianshe & Li, Xiaoxiao & Jiao, Licheng & Wang, Xiaohua & Sun, Bo, 2013. "Minimum spanning trees for community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2265-2277.
    5. Shang, Ronghua & Liu, Huan & Jiao, Licheng, 2017. "Multi-objective clustering technique based on k-nodes update policy and similarity matrix for mining communities in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 1-24.

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