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Alternating between consensus and leader selection reveals community structure in networks

Author

Listed:
  • Yang, Bo
  • Li, Xu
  • Liu, Xiangwei
  • He, He
  • Chen, Wei

Abstract

In this paper, we propose two novel algorithms to detect community structure in networks based on consensus dynamics. The first algorithm identifies the communities in networks by alternating between recognizing leader nodes following the analysis of influence coefficients of nodes, and finding the nodes belonging to the groups of their corresponding leader nodes using consensus dynamics and the difference coefficients of nodes. The second algorithm is an extension to the first one via the leader-following models. After confirming the leader nodes according to the first algorithm, we reveal the memberships of nodes belonging to the corresponding leaders by performing consensus dynamics. In the second algorithm, an approach to calculating the memberships of nodes is proposed. The corresponding leader nodes of communities can be confirmed naturally and the status of nodes in networks can be determined quantitatively. Finally, our algorithms are applied to real-world and computer generated networks whose community structures are well known. The experiment results show the effectiveness and reliability of the proposed algorithms.

Suggested Citation

  • Yang, Bo & Li, Xu & Liu, Xiangwei & He, He & Chen, Wei, 2019. "Alternating between consensus and leader selection reveals community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 693-706.
    • Handle: RePEc:eee:phsmap:v:515:y:2019:i:c:p:693-706
    DOI: 10.1016/j.physa.2018.10.003
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    References listed on IDEAS

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    1. Yang, Bo & He, He & Hu, Xiaoming, 2017. "Detecting community structure in networks via consensus dynamics and spatial transformation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 156-170.
    2. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    3. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    4. Capocci, A. & Servedio, V.D.P. & Caldarelli, G. & Colaiori, F., 2005. "Detecting communities in large networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 352(2), pages 669-676.
    5. Michelle Girvan & M. E. J. Newman, 2001. "Community Structure in Social and Biological Networks," Working Papers 01-12-077, Santa Fe Institute.
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