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Detect local communities in networks with an outside rate coefficient

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  • Shen, Yi

Abstract

In this paper, we present a local method for detecting communities in networks. We define an outside rate coefficient ψout in our method. ψout has a very simple form and is easy to calculate. The local community enclosing a starting node can be detected by agglomerating the node with the smallest ψout at each time step. When there are two or more nodes having the same smallest outside rate coefficient ψout, we agglomerate the node with the largest kin. This operation is remarkably beneficial to the accuracy of our method, and simulations on benchmark networks and real networks demonstrate that our local method is efficient to detect communities in networks.

Suggested Citation

  • Shen, Yi, 2013. "Detect local communities in networks with an outside rate coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(12), pages 2821-2829.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:12:p:2821-2829
    DOI: 10.1016/j.physa.2013.03.001
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    References listed on IDEAS

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    1. Bommarito, Michael J. & Katz, Daniel Martin & Zelner, Jonathan L. & Fowler, James H., 2010. "Distance measures for dynamic citation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(19), pages 4201-4208.
    2. Ren, Fu-Xin & Shen, Hua-Wei & Cheng, Xue-Qi, 2012. "Modeling the clustering in citation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(12), pages 3533-3539.
    3. Grindrod, Peter & Parsons, Mark, 2011. "Social networks: Evolving graphs with memory dependent edges," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3970-3981.
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    Cited by:

    1. Lin, Yi & Zhang, Jianwei & Yang, Bo & Liu, Hong & Zhao, Liping, 2019. "An optimal routing strategy for transport networks with minimal transmission cost and high network capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 551-561.

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