MODELLING COLLABORATION NETWORKS BASED ON NONLINEAR PREFERENTIAL ATTACHMENT

Tao Zhou (), Bing-Hong Wang, Ying-Di Jin, Da-Ren He, Pei-Pei Zhang, Yue He, Bei-Bei Su, Kan Chen, Zhong-Zhi Zhang and Jian-Guo Liu
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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

International Journal of Modern Physics C (IJMPC), 2007, vol. 18, issue 02, 297-314

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.

Keywords: Complex networks; collaboration network model; nonlinear preferential attachment; 89.75.Hc; 64.60.Ak; 84.35.+i; 05.40.-a; 05.50+q; 87.18.Sn (search for similar items in EconPapers)
Date: 2007
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0129183107010437

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