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Random pseudofractal networks with competition

Author

Listed:
  • Wang, Lei
  • Dai, Hua-ping
  • Sun, You-xian

Abstract

In this paper, we present a simple rule which assigns fitness to each edge to generate random pseudofractal networks (RPNs). This RPN model is both scale-free and small-world. We obtain the theoretical results that the power-law exponent is γ=2+1/(1+α) for the tunable parameter α>-1, and that the degree distribution is of an exponential form for others. Analytical results also show that an RPN has a large clustering coefficient and can process hierarchical structure as C(k)∼k-1 that is in accordance with many real networks. And we prove that the mean distance L(N) scales slower logarithmically with network size N. In particular, we explain the effect of nodes with degree 2 on the clustering coefficient. These results agree with numerical simulations very well.

Suggested Citation

  • Wang, Lei & Dai, Hua-ping & Sun, You-xian, 2007. "Random pseudofractal networks with competition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(2), pages 763-772.
    • Handle: RePEc:eee:phsmap:v:383:y:2007:i:2:p:763-772
    DOI: 10.1016/j.physa.2007.02.115
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    Citations

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    Cited by:

    1. Sun, Lina & Huang, Ning & Li, Ruiying & Bai, Yanan, 2019. "A new fractal reliability model for networks with node fractal growth and no-loop," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 699-707.
    2. Zhang, Yue & Huang, Ning & Xing, Liudong, 2016. "A novel flux-fluctuation law for network with self-similar traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 299-310.

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