Weighted self-similar networks under preferential attachment

被引：5

作者：

Chrysafis, O. ^{[1
]}

Cannings, C. ^{[1
]}

机构：

[1] Univ Sheffield, Sch Math & Stat, Sheffield S3 7RH, S Yorkshire, England

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2009年 / 388卷 / 14期

基金：

英国工程与自然科学研究理事会;

关键词：

Weighted networks; Degree correlations; Scale-free; EVOLUTION; DISTRIBUTIONS; INTERNET; TOPOLOGY; DIAMETER;

D O I：

10.1016/j.physa.2009.03.030

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

We introduce an abstract evolutionary formalism that generates weighted networks whose growth under stochastic preferential attachment triggers unrestricted weight rearrangements in existing links. The class of resulting algorithms for different parameter values includes the Barabasi-Albert and Barrat-Barthelemy-Vespignani models as special cases. We solve the recursions that describe the average growth to derive exact solutions for the expected degree and strength distribution, the individual strength and weight development and the joint distribution of neighboring degrees. We find that the network exhibits a particular form of self-similarity, namely every sufficiently interconnected node has on average the same constitution of small-degree neighbors as any other node of large degree. Finally we suggest potential applications in several fields of interest. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：2965 / 2974

页数：10

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