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Detecting Community Structure of Complex Networks Based on Network Potential

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Communications, Signal Processing, and Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 202))

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Abstract

Community structure is an important topological property of complex networks, which is beneficial to understand the structures and functions of complex networks. In this chapter a new statistical parameter, which we call network potential, is proposed to measure a complex network by introducing the field theories of physics. We then present a detecting algorithm of community structure based on the network potential whose main strategy is partitioning the network by optimizing the value of the network potential. We test our algorithm on both computer-generated networks and real-world networks whose community structure is already known. The experimental results show the algorithm can be effectively utilized for detecting the community structure of complex network.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61071152), National “Twelfth Five-Year” Plan for Science and Technology Support (2012BAH38B04), and Major State Basic Research Development Program (2010CB731400 and 2010CB731406).

Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai, 200240, China

    Yuxin Zhao, Xiuzhen Chen, Shenghong Li, Hao Peng & Yueguo Zhang

  2. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Chenglin Zhao

Authors
  1. Yuxin Zhao
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  2. Chenglin Zhao
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  3. Xiuzhen Chen
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  4. Shenghong Li
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  5. Hao Peng
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  6. Yueguo Zhang
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Corresponding author

Correspondence to Shenghong Li .

Editor information

Editors and Affiliations

  1. University of Texas at Arlington, 416 Yates St, Rm 518, Box 19016, Arlington, 76019-0016, Texas, USA

    Qilian Liang

  2. College of Physical and Electronic Infor, Tianjin Normal University, Bingshui West Road, XiQing District, Tianjin, 300387, China, People's Republic

    Wei Wang

  3. College of Physical and Electronic Infor, Tianjin Normal University, Bingshui West Road, XiQing District, Tianjin, 300387, China, People's Republic

    Jiasong Mu

  4. School of electronic engineering, University of Electronic Science and Tec, Xiyuan Road, Gaoxin District, Chengdu, 611731, China, People's Republic

    Jing Liang

  5. College of Physical and Electronic Infor, Tianjin Normal University, Bingshui West Road, XiQing District, Tianjin, 300387, China, People's Republic

    Baoju Zhang

  6. School of Electronic Engineering, University of Electronic Science and Tec, Xiyuan Road, Gaoxin district, Chengdu, 611731, China, People's Republic

    Yiming Pi

  7. School of Information and Communication, Beijing University of Posts and Telecomm, Xitucheng Road 10, Beijing, 100876, China, People's Republic

    Chenglin Zhao

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Zhao, Y., Zhao, C., Chen, X., Li, S., Peng, H., Zhang, Y. (2012). Detecting Community Structure of Complex Networks Based on Network Potential. In: Liang, Q., et al. Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 202. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5803-6_45

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  • DOI: https://doi.org/10.1007/978-1-4614-5803-6_45

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-5802-9

  • Online ISBN: 978-1-4614-5803-6

  • eBook Packages: EngineeringEngineering (R0)

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