Weighted Networks Model Based on Traffic Dynamics with Local Perturbation

来源 :Communications in Theoretical Physics | 被引量 : 0次 | 上传用户:yeyuxx008
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In the study of weighted complex networks,the interplay between traffic and topology have been paidmuch attention.However,the variation of topology and weight brought by new added vertices or edges should also beconsidered.In this paper,an evolution model of weighted networks driven by traffic dynamics with local perturbation isproposed.The model gives power-law distribution of degree,weight and strength,as confirmed by empirical measure-ments.By choosing appropriate parameters W and 5,the exponents of various power law distributions can be adjustedto meet real world networks.NontriviM clustering coefficient C,degree assortativity coefficient r,and strength-degreecorrelation are also considered.What should be emphasized is that,with the consideration of local perturbation,onecan adjust the exponent of strength-degree correlation more effectively.It makes our model more general than previousones and may help reproducing real world networks more appropriately. In the study of weighted complex networks, the interplay between traffic and topology have been paidmuch attention. Yet, the variation of topology and weight brought by new added vertices or edges should also be con-sidered. In this paper, an evolution model of weighted networks driven by traffic dynamics with local perturbation isproposed.The model gives power-law distribution of degree, weight and strength, as confirmed by empirical measure-ments.By please appropriate parameters W and 5, the exponents of various power law distributions can be adjustedto meet real world networks.NontriviM clustering coefficient C, degree assortativity coefficient r, and strength-degreecorrelation are also considered.What should be emphasized is that, with the consideration of local perturbation, onecan adjust the exponent of strength-degree correlation more effectively. It makes our model more general than previous and may help reproducing real world networks more appropriately.
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