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Limitation of multi-resolution methods in community detection

Author

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  • Xiang, Ju
  • Hu, Ke

Abstract

Community detection is of considerable interest for analyzing the structure and function of complex networks. Recently, a type of multi-resolution methods in community detection was introduced, which can adjust the resolution of modularity by modifying the modularity function with tunable resolution parameters, such as those proposed by Arenas, Fernández and Gómez and by Reichardt and Bornholdt. In this paper, we show that these methods still have the intrinsic limitation–large communities may have been split before small communities become visible–because it is at the cost of the community stability that the enhancement of the modularity resolution is obtained. The theoretical results indicated that the limitation depends on the degree of interconnectedness of small communities and the difference between the sizes of small communities and of large communities, while independent of the size of the whole network. These findings have been confirmed in several example networks, where communities even are full-completed sub-graphs.

Suggested Citation

  • Xiang, Ju & Hu, Ke, 2012. "Limitation of multi-resolution methods in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4995-5003.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:20:p:4995-5003
    DOI: 10.1016/j.physa.2012.05.006
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    Citations

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    Cited by:

    1. Ke Hu & Ju Xiang & Yun-Xia Yu & Liang Tang & Qin Xiang & Jian-Ming Li & Yong-Hong Tang & Yong-Jun Chen & Yan Zhang, 2020. "Significance-based multi-scale method for network community detection and its application in disease-gene prediction," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-24, March.
    2. Xiang, Ju & Tang, Yan-Ni & Gao, Yuan-Yuan & Zhang, Yan & Deng, Ke & Xu, Xiao-Ke & Hu, Ke, 2015. "Multi-resolution community detection based on generalized self-loop rescaling strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 127-139.
    3. Xiang, Ju & Hu, Tao & Zhang, Yan & Hu, Ke & Li, Jian-Ming & Xu, Xiao-Ke & Liu, Cui-Cui & Chen, Shi, 2016. "Local modularity for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 451-459.
    4. Laszlo Gadar & Zsolt T. Kosztyan & Janos Abonyi, 2018. "The Settlement Structure Is Reflected in Personal Investments: Distance-Dependent Network Modularity-Based Measurement of Regional Attractiveness," Complexity, Hindawi, vol. 2018, pages 1-16, December.
    5. Laszlo Gadar & Janos Abonyi, 2018. "Graph configuration model based evaluation of the education-occupation match," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-19, March.

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