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Application Of Cut Algorithm Based On Algebraic Connectivity To Community Detection

Author

Listed:
  • FUQIANG ZHAO

    (Department of Information Science and Technology, Tianjin University of Finance and Economics, Tianjin 300222, P. R. China)

  • LICHAO ZHANG

    (Department of Information Science and Technology, Tianjin University of Finance and Economics, Tianjin 300222, P. R. China)

  • GUIJUN YANG

    (#x2020;Department of Statistics, Tianjin University of Finance and Economics, Tianjin 300222, P. R. China)

  • LI HE

    (Department of Information Science and Technology, Tianjin University of Finance and Economics, Tianjin 300222, P. R. China)

  • FENGYU YAN

    (Department of Information Science and Technology, Tianjin University of Finance and Economics, Tianjin 300222, P. R. China)

Abstract

In the graph of a complex network, the algebraic connectivity is the second smallest eigenvalue of a Laplacian matrix. In this paper, we present a cut algorithm based on edge centrality by minimizing the algebraic connectivity of graph. The edge centrality cut algorithm (ECCA) cuts k edges at a time in order to reduce temporal complexity, the algebraic connectivity of which experiences the fastest decline. To prevent nodes from overcutting, each edge sets the weight. We use the advanced ECCA (AECCA) to detect overlapping communities by calculating the correlation coefficients of the nodes. This paper also proposes upper, lower and weaker lower bounds of algebraic connectivity. We demonstrate that our algorithms are effective and accurate at discovering community structure in both artificial and real-world network data and that the algebraic connectivity of the cut algorithm lies between the upper and lower bounds. Our algorithms offer new insights into community detection by calculating the edge centrality.

Suggested Citation

  • Fuqiang Zhao & Lichao Zhang & Guijun Yang & Li He & Fengyu Yan, 2017. "Application Of Cut Algorithm Based On Algebraic Connectivity To Community Detection," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 20(01), pages 1-18, February.
    • Handle: RePEc:wsi:acsxxx:v:20:y:2017:i:01:n:s0219525917500023
    DOI: 10.1142/S0219525917500023
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    References listed on IDEAS

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    1. 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.
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