基于次近邻扩散聚集生长的复杂网络及其分析

New Complex Network Model Based on Diffusion Aggregation with Next-Nearest Neighbor Growth Mechanism and Its Analysis

提出了一种基于次近邻扩散聚集生长的复杂网络模型生成法则.用于刻画与扩散置限凝聚等复杂现象相关的实测对象.通过细致的计算机仿真模拟,初步研究了生成半随机复杂网络的一些典型数字特征:包括度分布、网络直径、平均路径长度、平均聚类系数等.研究结果表明,按照提出的方法产生的复杂网络具有短的平均路径长度、较高的平均聚类系数、具备明显的齐次网络特性.同时,在变动跳动概率之差时,生成网络直径、平均路径长度、网络平均聚类系数等呈现规律性变化;且生成网络直径、平均路径长度与网络平均聚类系数呈负相关关系.

A new complex network model based on diffusion aggregation with next - nearest neighbor growth rsechanisrs is proposed for characterizing some natural complex behavior such as diffusion aggregation with next- nearest neighbur, etc. Some typical characters of the generated networks are carefully investigated through computer simulations, including the network diameter, the average path length, the degree distribution, clustering ouefficient, etc. Our research shows that the complex network produced by our method i,as short average path length, high clustering coefficient, and some typical characters of a homogeneous network. We also found that, the diameter, average path length and clustering coefficient have a regular change with different jumping probabilities chosen in our numerical experiments. Furthermore, the change of those characters with deviation between jumping probability Q and S are also investigated. The results show that the change of network diameter, average path length are negative correlation with that of the network clustering coefficieut.