Evolution of network from node division and generation

Aimed at lowering the effect of `rich get richer' in scale-free networks with the Barab\'{a}si and Albert model, this paper proposes a new evolving mechanism, which includes dividing and preference attachment for the growth of a network. A broad scale characteristic which is independent of the initial network topology is obtained with the proposed model. By simulating, it is found that preferential attachment causes the appearance of the scale-free characteristic, while the dividing will decrease the power-law behaviour and drive the evolution of broad scale networks.     

Immunization for scale-free networks by random walker

Based on the random walk and the intentional random walk, we propose two types of immunization strategies which require only local connectivity information. On several typical scale-free networks, we demonstrate that these strategies can lead to the eradication of the epidemic by immunizing a small fraction of the nodes in the networks. Particularly, the immunization strategy based on the intentional random walk is extremely efficient for the assortatively mixed networks.     

Normalized entropy of rank distribution： a novel measure of heterogeneity of complex networks

Many unique properties of complex networks result from heterogeneity. The measure and analysis of heterogeneity are important and desirable to the research of the properties and functions of complex networks. In this paper, the rank distribution is proposed as a new statistic feature of complex networks. Based on the rank distribution, a novel measure of the heterogeneity called a normalized entropy of rank distribution (NERD) is proposed. The NERD accords with the normal meaning of heterogeneity within the context of complex networks compared with conventional measures. The heterogeneity of scale-free networks is studied using the NERD. It is shown that scale-free networks become more heterogeneous as the scaling exponent decreases and the NERD of scale-free networks is independent of the number of vertices, which indicates that the NERD is a suitable and effective measure of heterogeneity for networks with different sizes.     

基于无标度网络拓扑结构变化的舆论演化模型

基于BA无标度网络,构建了舆论和网络结构相互影响的自适应舆论演化模型.舆论的演化不仅受制于网络拓扑结构,而且也导致拓扑结构的变化.研究表明,演化达到稳态后,网络结构不再是初始的无标度网络而呈现泊松分布,而且随着时间的推移,系统中的舆论演化表现出很强的趋同效应,原来初始状态的几十个舆论,在长时间的演化后,大部分舆论灭亡,只有少数的舆论存留,且发展壮大.这种趋向与社会上的舆论、意见、信仰的演化大体上是符合的.     

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.     

节点数加速增长的复杂网络生长模型

受某些实际网络节点数按几何级数增长现象的启发，构造了每个时间步中按当前网络规模成比例地同时加入多个节点的节点数加速增长的网络模型.研究表明，在增长率不是很大的情况下网络度分布仍然是幂律的，但在不同的增长率r下幂律指数是不同的.得到了幂律指数介于2到3之间可调的无标度网络模型，并解析地给出了幂律指数随增长率变化的函数关系.数值模拟还显示，网络的平均最短距离随r减小而簇系数随r增大. 关键词： 复杂网络 无标度网络 生长网络模型 节点数加速增长网络模型     

Natural networks as thermodynamic systems

Natural networks are considered as thermodynamic systems that evolve from one state to another by consuming free energy. The least-time consumption of free energy is found to result in ubiquitous scale-free characteristics. The network evolution will yield the scale-independent qualities because the least-time imperative will prefer attachment of nodes that contribute most to the free-energy consumption. The analysis of evolutionary equation of motion, derived from statistical physics of open systems, reveals that evolution of natural networks is a path-dependent and nondeterministic process. Despite the noncomputability of evolution, many mathematical models of networks can be recognized as approximations of the least-time process as well as many measures of networks can be appreciated as practical assessments of the system's thermodynamic status. © 2012 Wiley Periodicals, Inc. Complexity, 2012     

一种可大范围调节聚类系数的加权无标度网络模型

在Barrat, Barthélemy 和 Vespignani (BBV)加权无标度网络模型的基础上，提出了一种可大范围调节聚类系数的加权无标度网络模型——广义BBV模型(GBBV模型).理论分析和仿真实验表明，GBBV模型保留了BBV模型的许多特征，节点度、节点权重和边权值等都服从幂律分布.但是，GBBV模型克服了BBV模型只能小范围调节聚类系数的缺陷，从而可以用于具有大聚类系数网络的建模. 关键词： 无标度网络 加权网络 聚类系数     

肿瘤免疫无标度网络模型平衡点存在性及应用

应用无标度网络,建立肿瘤-免疫网络模型,来刻画免疫效应细胞、肿瘤细胞与细胞因子相互作用的机理。研究了网络模型的肿瘤平衡点在免疫效应细胞与细胞因子度关联与度非关联两种情况下的存在条件.得到了根据所得到的肿瘤平衡点存在的临界值,根据临界值提出免疫途径与方法,为抑制或预测肿瘤生长提供的理论参考.