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Fuzzy nodes recognition based on spectral clustering in complex networks

Author

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  • Ma, Yang
  • Cheng, Guangquan
  • Liu, Zhong
  • Xie, Fuli

Abstract

In complex networks, information regarding the nodes is usually incomplete because of the effects of interference, noise, and other factors. This results in parts of the network being blurred and some information having an unknown source. In this paper, a spectral clustering algorithm is used to identify fuzzy nodes and solve network reconstruction problems. By changing the fuzzy degree of placeholders, we achieve various degrees of credibility and accuracy for the restored network. Our approach is verified by experiments using open source datasets and simulated data.

Suggested Citation

  • Ma, Yang & Cheng, Guangquan & Liu, Zhong & Xie, Fuli, 2017. "Fuzzy nodes recognition based on spectral clustering in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 792-797.
    • Handle: RePEc:eee:phsmap:v:465:y:2017:i:c:p:792-797
    DOI: 10.1016/j.physa.2016.08.022
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    References listed on IDEAS

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    1. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
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    Cited by:

    1. Gianfranco Minati, 2020. "Complex Cognitive Systems and Their Unconscious. Related Inspired Conjectures for Artificial Intelligence," Future Internet, MDPI, vol. 12(12), pages 1-24, November.
    2. Karimi-Majd, Amir-Mohsen & Fathian, Mohammad & Makrehchi, Masoud, 2018. "Consensus-based methodology for detection communities in multilayered networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 547-558.

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