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Detecting communities in social networks using label propagation with information entropy

Author

Listed:
  • Chen, Naiyue
  • Liu, Yun
  • Chen, Haiqiang
  • Cheng, Junjun

Abstract

Community detection has become an important and effective methodology to understand the structure and function of real world networks. The label propagation algorithm (LPA) is a near-linear time algorithm used to detect non-overlapping community. However, it merely considers the direct neighbor relationship. In this paper, we propose an algorithm to consider information entropy as the measurement of the relationship between direct neighbors and indirect neighbors. In a label update, we proposed a new belonging coefficient to describe the weight of the label. With the belonging coefficient no less than a threshold each node can keep one or more labels to constitute an overlapping community. Experimental results on both real-world and benchmark networks show that our algorithm also possesses high accuracy on detecting community structure in networks.

Suggested Citation

  • Chen, Naiyue & Liu, Yun & Chen, Haiqiang & Cheng, Junjun, 2017. "Detecting communities in social networks using label propagation with information entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 788-798.
    • Handle: RePEc:eee:phsmap:v:471:y:2017:i:c:p:788-798
    DOI: 10.1016/j.physa.2016.12.047
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    References listed on IDEAS

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    1. Eustace, Justine & Wang, Xingyuan & Cui, Yaozu, 2015. "Community detection using local neighborhood in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 665-677.
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    Cited by:

    1. Garza, Sara E. & Schaeffer, Satu Elisa, 2019. "Community detection with the Label Propagation Algorithm: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    2. Mustafa Alassad & Muhammad Nihal Hussain & Nitin Agarwal, 2022. "Comprehensive decomposition optimization method for locating key sets of commenters spreading conspiracy theory in complex social networks," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(1), pages 367-394, March.
    3. Hosseini-Pozveh, Maryam & Ghorbanian, Maedeh & Tabaiyan, Maryam, 2022. "A label propagation-based method for community detection in directed signed social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    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. Ma, Tinghuai & Yue, Mingliang & Qu, Jingjing & Tian, Yuan & Al-Dhelaan, Abdullah & Al-Rodhaan, Mznah, 2018. "PSPLPA: Probability and similarity based parallel label propagation algorithm on spark," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 366-378.
    6. Hao Xu & Yuan Ran & Junqian Xing & Li Tao, 2023. "An Influence-Based Label Propagation Algorithm for Overlapping Community Detection," Mathematics, MDPI, vol. 11(9), pages 1-17, May.
    7. Zhang, Xuguang & Shu, Xiaohu & He, Zhen, 2019. "Crowd panic state detection using entropy of the distribution of enthalpy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 935-945.

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