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Scale-free networks by super-linear preferential attachment rule

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  • Wu, Liang
  • Zhu, Shiqun

Abstract

A network growth model with geographic limitation of accessible information about the status of existing nodes is investigated. In this model, the probability Π(k) of an existing node of degree k is found to be super-linear with Π(k)∼kα and α>1 when there are links from new nodes. The numerical results show that the constructed networks have typical power-law degree distributions P(k)∼k−γ and the exponent γ depends on the constraint level. An analysis of local structural features shows the robust emergence of scale-free network structure in spite of the super-linear preferential attachment rule. This local structural feature is directly associated with the geographical connection constraints which are widely observed in many real networks.

Suggested Citation

  • Wu, Liang & Zhu, Shiqun, 2008. "Scale-free networks by super-linear preferential attachment rule," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3789-3795.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:14:p:3789-3795
    DOI: 10.1016/j.physa.2008.01.030
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