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Related Experiment Video

Updated: May 26, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

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Published on: September 25, 2021

Memetic algorithm for community detection in networks.

Maoguo Gong1, Bao Fu, Licheng Jiao

  • 1Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi Province, China.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 21, 2011
PubMed
Summary

This study introduces Meme-Net, a novel memetic algorithm for community detection in networks. Meme-Net optimizes modularity density, overcoming the resolution limit of traditional modularity optimization, and demonstrates effectiveness across various networks.

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Last Updated: May 26, 2026

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09:49

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Published on: September 25, 2021

Area of Science:

  • Network science
  • Computational complexity
  • Data mining

Background:

  • Community structure is a fundamental network property.
  • Modularity optimization is a common community detection method.
  • Modularity optimization suffers from a resolution limit, hindering detection of communities at different scales.

Purpose of the Study:

  • To address the resolution limit in network community detection.
  • To introduce a novel algorithm, Meme-Net, for optimizing modularity density.
  • To enable multiresolution community detection in networks.

Main Methods:

  • Developed Meme-Net, a memetic algorithm combining genetic algorithms with hill-climbing.
  • Utilized modularity density as the quality function for community detection.
  • Employed a tunable parameter within modularity density for multiresolution analysis.

Main Results:

  • Meme-Net effectively detects communities in both synthetic and real-world networks.
  • The algorithm demonstrates superior performance compared to traditional methods by overcoming the resolution limit.
  • The tunable parameter allows for exploration of community structures at various resolutions.

Conclusions:

  • Meme-Net offers an effective and flexible approach to community detection.
  • The proposed method successfully addresses the resolution limit problem inherent in modularity optimization.
  • This work provides a valuable tool for analyzing complex networks at multiple scales.