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Application of natural computation inspired method in community detection

Author

Listed:
  • Zhang, Weitong
  • Zhang, Rui
  • Shang, Ronghua
  • Li, Juanfei
  • Jiao, Licheng

Abstract

The study of community structure in complex networks has always been a subject of great concern in various fields. Community structure can reflect the dynamic characteristics and functions of complex networks. In recent years, there has been numerous methods proposed for community detection. Natural computing methods are inspired by nature Which have the ability of self-adaptation, self-organization and self-learning. This kind of methods can solve the complex problem that traditional calculation method cannot solve. With the effective network partition evaluation function proposed, the community detection problem can also be regarded as a kind of optimization problem. Therefore, natural computing methods are widely applied in community detection. This paper summarizes the application of natural computing inspired method in community detection, and briefly introduces its basic framework and development course.

Suggested Citation

  • Zhang, Weitong & Zhang, Rui & Shang, Ronghua & Li, Juanfei & Jiao, Licheng, 2019. "Application of natural computation inspired method in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 130-150.
    • Handle: RePEc:eee:phsmap:v:515:y:2019:i:c:p:130-150
    DOI: 10.1016/j.physa.2018.09.186
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    2. Shang, Ronghua & Li, Ruolin & Wang, Chi & Zhang, Weitong & Xu, Songhua & Feng, Dongzhu, 2024. "Node classification based on Attribute Fuse Edge Features and Label Adaptive Adjustment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 654(C).

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