Scale-free networks by super-linear preferential attachment rule

Liang Wu and Shiqun Zhu

Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 14, 3789-3795

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.

Keywords: Random graph; Scale-free; Super-linear preferential attachment (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:387:y:2008:i:14:p:3789-3795

DOI: 10.1016/j.physa.2008.01.030

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