Multistage Random Growing Small-World Networks with Power-Law Degree Distribution  

作　　者：刘建国 党延忠 王众托 

机构地区：[1]Institute of System Engineering, Dalian University of Technology, Dalian 116023

出　　处：《Chinese Physics Letters》2006年第3期746-749,共4页中国物理快报（英文版）

基　　金：Supported by the National Natural Science Foundation of China under Grant Nos 70431001 and 70271046.

摘　　要：We present a simple rule which could generate scale-free networks with very large clustering coefficient and very small average distance. These networks, called the multistage random growing networks （MRGNs）, are constructed by a two-stage adding process for each new node. The analytic results of the power-law exponent = 3 and the clustering coefficient C = 0.81 are obtained, which agree with the simulation results approximately. In addition, we find that the average distance of the networks increases logarithmically with the network size, which is consistent with the theoretical predictions. Since many real-world networks are both scale-free and small-world, the MRGNs may perform well in mimicking reality.We present a simple rule which could generate scale-free networks with very large clustering coefficient and very small average distance. These networks, called the multistage random growing networks （MRGNs）, are constructed by a two-stage adding process for each new node. The analytic results of the power-law exponent = 3 and the clustering coefficient C = 0.81 are obtained, which agree with the simulation results approximately. In addition, we find that the average distance of the networks increases logarithmically with the network size, which is consistent with the theoretical predictions. Since many real-world networks are both scale-free and small-world, the MRGNs may perform well in mimicking reality.

关 键 词：SCALE-FREE NETWORKS US FLIGHT NETWORK COMPLEX NETWORKS DYNAMICS OPTIMIZATION ROBUSTNESS MODEL 

分 类 号：TP393.01[自动化与计算机技术—计算机应用技术]