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Modelling Collaboration Networks Based On Nonlinear Preferential Attachment

Author

Listed:
  • TAO ZHOU

    (Nonlinear Science Center and Department of Modern Physics, University of Science and Technology of China, Hefei Anhui, 230026, P. R. China)

  • BING-HONG WANG

    (Nonlinear Science Center and Department of Modern Physics, University of Science and Technology of China, Hefei Anhui, 230026, P. R. China)

  • YING-DI JIN

    (Nonlinear Science Center and Department of Modern Physics, University of Science and Technology of China, Hefei Anhui, 230026, P. R. China)

  • DA-REN HE

    (College of Physical Science and Technology, Yangzhou University, Yangzhou Jiangsu, 225002, P. R. China)

  • PEI-PEI ZHANG

    (College of Physical Science and Technology, Yangzhou University, Yangzhou Jiangsu, 225002, P. R. China)

  • YUE HE

    (College of Physical Science and Technology, Yangzhou University, Yangzhou Jiangsu, 225002, P. R. China)

  • BEI-BEI SU

    (College of Physical Science and Technology, Yangzhou University, Yangzhou Jiangsu, 225002, P. R. China)

  • KAN CHEN

    (Department of Computational Science, Faculty of Science, National University of Singapore, Singapore 117543, Singapore)

  • ZHONG-ZHI ZHANG

    (Institute of Systems Engineering, Dalian University of Technology, Dalian Liaoning, 116024, P. R. China)

  • JIAN-GUO LIU

    (Institute of Systems Engineering, Dalian University of Technology, Dalian Liaoning, 116024, P. R. China)

Abstract

In this paper, we propose an alternative model for collaboration networks based on nonlinear preferential attachment. Depending on a single free parameter "preferential exponent", this model interpolates between networks with a scale-free and an exponential degree distribution. The degree distribution in the present networks can be roughly classified into four patterns, all of which are observed in empirical data. And this model exhibits small-world effect, which means the corresponding networks are of very short average distance and highly large clustering coefficient. More interesting, we find a peak distribution of act-size from empirical data which has not been emphasized before. Our model can produce the peak act-size distribution naturally that agrees with the empirical data well.

Suggested Citation

  • Tao Zhou & Bing-Hong Wang & Ying-Di Jin & Da-Ren He & Pei-Pei Zhang & Yue He & Bei-Bei Su & Kan Chen & Zhong-Zhi Zhang & Jian-Guo Liu, 2007. "Modelling Collaboration Networks Based On Nonlinear Preferential Attachment," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 297-314.
    • Handle: RePEc:wsi:ijmpcx:v:18:y:2007:i:02:n:s0129183107010437
    DOI: 10.1142/S0129183107010437
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    Cited by:

    1. Shen, Ai-Zhong & Guo, Jin-Li & Wu, Guo-Lin & Jia, Shu-Wei, 2018. "The agglomeration phenomenon influence on the scaling law of the scientific collaboration system," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 461-467.
    2. Zhang, Ke & Gao, Jingyu & Zhao, Haixing & Hu, Wenjun & Miao, Minmin & Zhang, Zi-Ke, 2025. "Uniform transformation and collective degree analysis on higher-order networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 666(C).

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