The classification and analysis of dynamic networks

In this paper we, firstly, classify the complex networks in which the nodes are of the lifetime distribution. Secondly, in order to study complex networks in terms of queuing system and homogeneous Markov chain, we establish the relation between the complex networks and queuing system, providing a new way of studying complex networks. Thirdly, we prove that there exist stationary degree distributions of M--G--P network, and obtain the analytic expression of the distribution by means of Markov chain theory. We also obtain the average path length and clustering coefficient of the network. The results show that M--G--P network is not only scale-free but also of a small-world feature in proper conditions.     

Scale-Free Properties in Traffic System

In this work, we propose a new model of evolution networks, which is based on the evolution of the traffic flow. In our method, the network growth does not take into account preferential attachment, and the attachment of new node is independent of the degree of nodes. Our aim is that employing the theory of evolution network, we give a further understanding about the dynamical evolution of the traffic flow. We investigate the probability distributions and scaling properties of the proposed model. The simulation results indicate that in the proposed model, the distribution of the output connections can be well described by scale-free distribution. Moreover, the distribution of the connections is largely related to the traffic flow states, such as the exponential distribution (i.e., the scale-free distribution) and random distribution etc.     

Modelling the spread of sexually transmitted diseases on scale-free networks

In this paper a new model for the spread of sexually transmitted diseases (STDs) is presented. The dynamic behaviors of the model on a heterogenous scale-free (SF) network are considered, where the absence of a threshold on the SF network is demonstrated, and the stability of the disease-free equilibrium is obtained. Three immunization strategies, uniform immunization, proportional immunization and targeted immunization, are applied in this model. Analytical and simulated results are given to show that the proportional immunization strategy in the model is effective on SF networks.     

Epidemic spreading in scale-free networks including the effect of individual vigilance

In this paper, we study the epidemic spreading in scale-free networks and propose a new susceptible-infected-recovered (SIR) model that includes the effect of individual vigilance. In our model, the effective spreading rate is dynamically adjusted with the time evolution at the vigilance period. Using the mean-field theory, an analytical result is derived. It shows that individual vigilance has no effect on the epidemic threshold. The numerical simulations agree well with the analytical result. Furthermore, we investigate the effect of individual vigilance on the epidemic spreading speed. It is shown that individual vigilance can slow the epidemic spreading speed effectively and delay the arrival of peak epidemic infection.     

两个网络间的相互同步

本文研究了两个耦合网络的相互同步,利用线性化方法,我们给出了两个具有相同拓扑结构的网络实现同步的定理,最后用数值例子来验证得到的理论结果。     

考虑个体保护意识的关联信用风险传染机理研究

针对关联信用风险及其传染这一热点和难点问题,本文基于复杂网络异质平均场理论,运用风险传播动力学SIR经典模型,探讨风险信息促成的个体保护意识对关联信用风险传染的影响机理,并在BA无标度网络中进行数值仿真分析。研究结果表明:被感染个体数量、个体反应强度、有保护意识的易感个体比例与关联信用风险传染阈值正相关;考虑个体保护意识、增强易感个体反应强度以及提高有保护意识的易感个体比例能够有效抑制关联信用风险的传染速度和传染规模,并且能够延缓关联信用风险高峰期的到来。     

Contact processes on scale-free networks

We investigate the contact process on random graphs generated from the configuration model for scale-free complex networks with the power law exponent β E （2, 3]. Using the neighborhood expansion method, we show that, with positive probability, any disease with an infection rate λ 〉 0 can survive for exponential time in the number of vertices of the graph. This strongly supports the view that stochastic scale-free networks are remarkably different from traditional regular graphs, such as, Z^d and classical Erdos-Renyi random graphs.     

网络"建筑学"

复杂网络系统普遍地存在于自然界与人类社会中，对它们的研究也具有广泛而重要的意义．我们如何来认识和区分晶格、Internet和一个生态食物链?它们又是如何演化的?在这些网络背后，是否隐藏着某种无形的组织原理?近年来，借助强大的计算工具，人们对网络的认识发生了巨大的改变，文章以小世界、集团化和无标度等新的概念为中心，介绍了其中的研究进展．     

Self-organized Criticality in a Modified Evolution Model on Generalized Barabási-Albert Scale-Free Networks

A modified evolution model of self-organized criticality on generalized Barabási-Albert (GBA) scale-free networks is investigated. In our model, we find that spatial and temporal correlations exhibit critical behaviors. More importantly, these critical behaviors change with the parameter b, which weights the distance in comparison with the degree in the GBA network evolution.     

大规模软件系统的无标度特性与演化模型

在软件工程中，常用类图来描述类之间的关系. 以软件系统网为研究对象，通过对Sun和IBM公司提供的大规模软件系统进行实证分析，发现Java软件系统网的度分布是无标度分布，标度指数γ≈2.5. 在软件系统网的演化过程中，除加点之外，还存在边的添加、边的随机移除与边的重连等局部事件. 由此建立了软件系统演化模型. 由该模型演化生成的网络，其度分布服从幂律分布. 实际应用与数值仿真验证了该模型的有效性. 关键词： 软件系统 复杂网络 度分布 无标度