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Community detection in complex networks by density-based clustering

Author

Listed:
  • Jin, Hong
  • Wang, Shuliang
  • Li, Chenyang

Abstract

We proposed a method to find the community structure in a complex network by density-based clustering. Physical topological distance is introduced in density-based clustering for determining a distance function of specific influence functions. According to the distribution of the data, the community structures are uncovered. The method keeps a better connection mode of the community structure than the existing algorithms in terms of modularity, which can be viewed as a basic characteristic of community detection in the future. Moreover, experimental results indicate that the proposed method is efficient and effective to be used for community detection of medium and large networks.

Suggested Citation

  • Jin, Hong & Wang, Shuliang & Li, Chenyang, 2013. "Community detection in complex networks by density-based clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4606-4618.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:19:p:4606-4618
    DOI: 10.1016/j.physa.2013.05.039
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    Citations

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    Cited by:

    1. You, Tao & Cheng, Hui-Min & Ning, Yi-Zi & Shia, Ben-Chang & Zhang, Zhong-Yuan, 2016. "Community detection in complex networks using density-based clustering algorithm and manifold learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 221-230.
    2. Seema Rani & Monica Mehrotra, 2019. "Community Detection in Social Networks: Literature Review," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 1-28, June.
    3. Zhou, Xu & Liu, Yanheng & Wang, Jian & Li, Chun, 2017. "A density based link clustering algorithm for overlapping community detection in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 65-78.
    4. Laassem, Brahim & Idarrou, Ali & Boujlaleb, Loubna & Iggane, M’bark, 2022. "Label propagation algorithm for community detection based on Coulomb’s law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    5. Li, Yafang & Jia, Caiyan & Yu, Jian, 2015. "A parameter-free community detection method based on centrality and dispersion of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 321-334.
    6. Li, Yafang & Jia, Caiyan & Li, Jianqiang & Wang, Xiaoyang & Yu, Jian, 2018. "Enhanced semi-supervised community detection with active node and link selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 219-232.
    7. Mehmet Ali Balcı & Larissa M. Batrancea & Ömer Akgüller & Anca Nichita, 2022. "Coarse Graining on Financial Correlation Networks," Mathematics, MDPI, vol. 10(12), pages 1-16, June.
    8. Wang, Tao & Yin, Liyan & Wang, Xiaoxia, 2018. "A community detection method based on local similarity and degree clustering information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1344-1354.
    9. Huizi Gu & Xiaodong Chu, 2020. "Quantifying Topological Flexibility of Active Distribution Networks Based on Community Detection," Energies, MDPI, vol. 13(18), pages 1-21, September.
    10. Lin, Zhen & Zheng, Xiaolin & Xin, Nan & Chen, Deren, 2014. "CK-LPA: Efficient community detection algorithm based on label propagation with community kernel," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 386-399.
    11. Xiaofeng Wang & Gongshen Liu & Jianhua Li & Jan P Nees, 2017. "Locating Structural Centers: A Density-Based Clustering Method for Community Detection," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-23, January.

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