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Detecting community structure in complex networks via resistance distance

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  • Zhang, Teng
  • Bu, Changjiang

Abstract

For two vertices i and j in a connected network G, the resistance distance between i and j is defined to be the effective resistance between them when unit resistors are placed on every edge of G. In this paper, by utilizing Gaussian function of the resistance distance between two vertices associated with each edge, the original network G is converted into weighted network G′. Next, applying the bisection spectral method, the G′ is divided into two subnetworks. And repeat this process in the subnetwork, the community structure of G is detected. Three real-world networks are used to test the proposed method. Experimental results demonstrate the feasibility and effectiveness by the proposed method in comparison with other community discovery methods.

Suggested Citation

  • Zhang, Teng & Bu, Changjiang, 2019. "Detecting community structure in complex networks via resistance distance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    • Handle: RePEc:eee:phsmap:v:526:y:2019:i:c:s0378437119303826
    DOI: 10.1016/j.physa.2019.04.018
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    Citations

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    Cited by:

    1. Wang, Tao & Chen, Shanshan & Wang, Xiaoxia & Wang, Jinfang, 2020. "Label propagation algorithm based on node importance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    2. Sardar, Muhammad Shoaib & Pan, Xiang-Feng & Xu, Si-Ao, 2020. "Computation of resistance distance and Kirchhoff index of the two classes of silicate networks," Applied Mathematics and Computation, Elsevier, vol. 381(C).
    3. Kwami Senam A. Sedzro & Kelsey Horowitz & Akshay K. Jain & Fei Ding & Bryan Palmintier & Barry Mather, 2021. "Evaluating the Curtailment Risk of Non-Firm Utility-Scale Solar Photovoltaic Plants under a Novel Last-In First-Out Principle of Access Interconnection Agreement," Energies, MDPI, vol. 14(5), pages 1-14, March.
    4. Gutiérrez, Caracé & Gancio, Juan & Cabeza, Cecilia & Rubido, Nicolás, 2021. "Finding the resistance distance and eigenvector centrality from the network’s eigenvalues," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    5. Lin, Wei & Li, Min & Zhou, Shuming & Liu, Jiafei & Chen, Gaolin & Zhou, Qianru, 2021. "Phase transition in spectral clustering based on resistance matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).

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