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Mobile user forecast and power-law acceleration invariance of scale-free networks

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【作者】 郭进利郭曌华刘雪娇

【Author】 Guo Jin-Li Guo Zhao-Hua Liu Xue-Jiao a)Business School,University of Shanghai for Science and Technology,Shanghai 200093,China b)College of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China

【机构】 Business School,University of Shanghai for Science and TechnologyCollege of Mechanical Engineering,University of Shanghai for Science and Technology

【摘要】 <正>This paper studies and predicts the number growth of China’s mobile users by using the power-law regression.We find that the number growth of the mobile users follows a power law.Motivated by the data on the evolution of the mobile users,we consider scenarios of self-organization of accelerating growth networks into scale-free structures and propose a directed network model,in which the nodes grow following a power-law acceleration.The expressions for the transient and the stationary average degree distributions are obtained by using the Poisson process.This result shows that the model generates appropriate power-law connectivity distributions.Therefore,we find a power-law acceleration invariance of the scale-free networks.The numerical simulations of the models agree with the analytical results well.

【Abstract】 This paper studies and predicts the number growth of China’s mobile users by using the power-law regression.We find that the number growth of the mobile users follows a power law.Motivated by the data on the evolution of the mobile users,we consider scenarios of self-organization of accelerating growth networks into scale-free structures and propose a directed network model,in which the nodes grow following a power-law acceleration.The expressions for the transient and the stationary average degree distributions are obtained by using the Poisson process.This result shows that the model generates appropriate power-law connectivity distributions.Therefore,we find a power-law acceleration invariance of the scale-free networks.The numerical simulations of the models agree with the analytical results well.

【基金】 supported by the National Natural Science Foundation of China(Grant No.70871082);the Shanghai Leading Academic Discipline Project,China(Grant No.S30504)
  • 【文献出处】 Chinese Physics B ,中国物理B , 编辑部邮箱 ,2011年11期
  • 【分类号】TN929.5
  • 【被引频次】1
  • 【下载频次】59
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