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

A modified evolution model of self-organized criticality on generalized Barabasi-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.

Malicious Code Modeling and Analysis in Weighted Scale-Free Networks

We study the detailed malicious code propagating process in scale-free networks with link weights that denotes traffic between two nodes. It is found that the propagating velocity reaches a peak rapidly then decays in a power-law form, which is different from the well-known result in unweighted network case. Simulation results show that the nodes with larger strength are preferential to be infected, but the hierarchical dynamics are not clearly found. The simulation results also show that larger dispersion of weight of networks leads to slower propagating, which indicates that malicious code propagates more quickly in unweighted scale-free networks than in weighted scale-free networks under the same condition. These results show that not only the topology of networks but also the link weights affect the malicious propagating process.

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 heterogenons 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.

Integrated systemic inflammatory response syndrome epidemic model in scale-free networks

Based on the scale-free network, an integrated systemic inflammatory response syndrome model with artificial immunity, a feedback mechanism, crowd density and the moving activities of an individual can be built. The effects of these factors on the spreading process are investigated through the model. The research results show that the artificial immunity can reduce the stable infection ratio and enhance the spreading threshold of the system. The feedback mechanism can only reduce the stable infection ratio of system, but cannot affect the spreading threshold of the system. The bigger the crowd density is, the higher the infection ratio of the system is and the smaller the spreading threshold is. In addition, the simulations show that the individual movement can enhance the stable infection ratio of the system only under the condition that the spreading rate is high, however, individual movement will reduce the stable infection ratio of the system.

A new evolutional model for institutional field knowledge flow network

Purpose:This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model(IKM).The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.Design/Methodology/Approach:The IKM model enhances the preferential attachment and growth observed in scale-free BA networks,while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network.To compare its performance,the BA and DMS models are also employed for simulating the network.Pearson coefficient analysis is conducted on the simulated networks generated by the IKM,BA and DMS models,as well as on the actual network.Findings:The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network.It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm.The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.Research Limitations:This study has some limitations.Firstly,it primarily focuses on the evolution of knowledge flow networks within the field of physics,neglecting other fields.Additionally,the analysis is based on a specific set of data,which may limit the generalizability of the findings.Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.Practical Implications:The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions.It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations.The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.Originality/value:This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks.The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions.Moreover,the model has the potential to be applied to other knowledge networks,which are formed by knowledge organizations as node units.

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

In this paper, we study the epidemic spreading in scale-flee 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. Purthermore, 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.

A Novel Neighbor-Preferential Growth Scale-Free Network Model and its Properties

In this paper, we propose a novel neighbor-preferential growth (NPG) network model. Theoretical analysis and numerical simulations indicate the new model can reproduce not only a scale-free degree distribution and its power exponent is related to the edge-adding number m, but also a small-world effect which has large clustering coefficient and small average path length. Interestingly, the clustering coefficient of the model is close to that of globally coupled network, and the average path length is close to that of star coupled network. Meanwhile, the synchronizability of the NPG model is much stronger than that of BA scale-free network, even stronger than that of synchronization-optimal growth network.

Random walk immunization strategy on scale-free networks

A novel immunization strategy called the random walk immunization strategy on scale-free networks is proposed. Different from other known immunization strategies, this strategy works as follows： a node is randomly chosen from the network. Starting from this node, randomly walk to one of its neighbor node; if the present node is not immunized, then immunize it and continue the random walk; otherwise go back to the previous node and randomly walk again. This process is repeated until a certain fraction of nodes is immunized. By theoretical analysis and numerical simulations, we found that this strategy is very effective in comparison with the other known immunization strategies.

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

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.

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.

Strategic games on a hierarchical network model

Among complex network models,the hierarchical network model is the one most close to such real networks as world trade web,metabolic network,WWW,actor network,and so on.It has not only the property of power-law degree distribution,but also the scaling clustering coefficient property which Barabási-Albert(BA)model does not have.BA model is a model of network growth based on growth and preferential attachment,showing the scale-free degree distribution property.In this paper,we study the evolution of cooperation on a hierarchical network model,adopting the prisoner's dilemma(PD)game and snowdrift game(SG)as metaphors of the interplay between connected nodes.BA model provides a unifying framework for the emergence of cooperation.But interestingly,we found that on hierarchical model,there is no sign of cooperation for PD game,while the fre-quency of cooperation decreases as the common benefit decreases for SG.By comparing the scaling clustering coefficient prop-erties of the hierarchical network model with that of BA model,we found that the former amplifies the effect of hubs.Considering different performances of PD game and SG on complex network,we also found that common benefit leads to cooperation in the evolution.Thus our study may shed light on the emergence of cooperation in both natural and social environments.

自适应BAS优化RBF神经网络的短时交通流预测

为提高短时交通流预测精度,针对传统径向基函数(radial basis function,RBF)神经网络短时交通流预测模型中心值固定、易受漂移数据干扰问题,提出自适应天牛须搜索算法(beetle antennae search algorithm,BAS)优化RBF神经网络的短时交通流预测模型。模型采用自适应步长提高BAS算法迭代速度和寻优能力,结合DBSCAN聚类确定RBF神经网络隐含层径向基函数网络中心,进而优化神经网络结构。通过路网真实交通流数据进行训练,选择常用于短时交通流预测的BP神经网络,RBF神经网络,广义RBF神经网络进行对比。结果表明:优化后的模型预测结果相较BP神经网络平均绝对误差降低了1.87%、平均绝对百分比误差降低了15.96%、均方根误差降低了3.24%,拟合度提高了3.96%;相较广义RBF神经网络平均绝对误差降低1.36%、平均绝对百分比误差降低了5.01%、均方根误差降低了2.19%,拟合度提高了2.5%。改进后的短时交通流预测模型能够为智能交通诱导提供可靠的预测值。

Stability of weighted spectral distribution in a pseudo tree-like network model

The comparison of networks with different orders strongly depends on the stability analysis of graph features in evolving systems. In this paper, we rigorously investigate the stability of the weighted spectral distribution（i.e., a spectral graph feature） as the network order increases. First, we use deterministic scale-free networks generated by a pseudo treelike model to derive the precise formula of the spectral feature, and then analyze the stability of the spectral feature based on the precise formula. Except for the scale-free feature, the pseudo tree-like model exhibits the hierarchical and small-world structures of complex networks. The stability analysis is useful for the classification of networks with different orders and the similarity analysis of networks that may belong to the same evolving system.

Synchronization of time-delay chaotic systems on small-world networks with delayed coupling

By using the well-known Ikeda model as the node dynamics, this paper studies synchronization of time-delay systems on small-world networks where the connections between units involve time delays. It shows that, in contrast with the undelayed case, networks with delays can actually synchronize more easily. Specifically, for randomly distributed delays, time-delayed mutual coupling suppresses the chaotic behaviour by stabilizing a fixed point that is unstable for the uncoupled dynamical system.

基于改进BAS算法的配电网多目标优化重构

天牛须搜索(BAS)算法存在早熟收敛、计算效率低下等缺点,文中提出一种改进BAS算法,并应用于解决配电网优化重构问题。首先建立以网络损耗、负荷均衡度、电压偏差以及系统供电可靠性为目标的数学模型;然后针对传统天牛须搜索算法易陷入局部最优,采用模拟退火的蒙特卡洛准则进行改进,提高了算法的稳定性和可重复性,并采用改进后的天牛须搜索算法对数学模型进行迭代求解,给出配电网最优重构方案;最后结合IEEE 69总线系统进行实例仿真分析,与未进行系统重构配置进行对比,所提算法减少了网络损耗,提高了系统运行的经济稳定性。

基于BA网络的突发事件下静态局部路由策略研究

为解决突发事件发生时,城市路网受到较大影响,甚至造成拥堵,文章基于无标度BA网络对路网建模,分析突发事件对节点通行能力的影响,进而通过对网络应用静态局部路由策略,分析不同程度突发事件下的最优路由策略参数,从而得出最佳路由策略。经仿真得出,优化后的模型提高网络临界信息包产生率,网络中数据包的分布更加均匀,路径选择更优,从而减少数据包平均传输时间,能够有效地解决突发事件下节点拥堵问题,使突发事件对网络通行能力的影响减小。

BA模型的三种扩展

无标度网络的发现,使人类对于复杂网络的认识进入了一个新的天地.BA模型是对于这种新认识的第1个抽象模型.由于考虑了系统的成长性和连接的择优性,BA模型给人们带来了很多启发.但是BA模型的两个基本假定,对于解释许多现实中的现象来说过于简单,与现实的网络还有较大的距离.所以,作者试图对BA模型进行扩展,即根据现实中的网络,增添某些假定,并通过计算机模拟进行观察,以便进一步探索复杂网络系统的规律.对BA模型的扩充考虑了3个因素:择优选择的成本;边的重新连接;网络的初始状态.实验结果表明,在考虑了这些因素之后,BA模型的基本结论并没有变化,它们的影响在于演化过程的某些方面,例如在某些极端条件下的演变趋势等.

基于改进BA模型的不同规模海运复杂网络演化研究

以复杂网络理论为基础,分析海运网络的拓扑结构具有无标度网络特性,可运用BA无标度网络模型构建演化海运网络.连接概率是BA模型中节点优先连接的重要依据,据此,针对海运网络港口节点进行研究,通过加权量化和MATLAB编程将影响节点间连接的因素组成节点吸引度,引入连接概率公式,改进了BA模型.分别选取2010年全球15个和25个主要集装箱港口的相关数据,运用上述改进BA模型分别得到不同规模的海运网络演化情况,演化结果验证了海运复杂网络具有无标度网络特征,呈现的特性与网络规模没有必然联系,规模大的网络平均路径更长、集聚性更强,度值相差更悬殊.进一步运用全球班轮航线实际网络进行验证,得到两者结构特性基本相同.

生态学视角下的集装箱海运网络适应度BA模型

集装箱海运网络主要反映的是港口与航线的空间组织,其演化是一种复杂的社会、经济、历史的整合过程。运用生态学的隐喻方法对其演化的动力机制进行剖析,充分考虑到现实集装箱海运网络的规模限制与动态发展的演化特征,提出基于"优胜劣汰"的五种机制:节点增加、节点删除、择优连接、反择优删除与重新连接;通过设计港口腹地经济、港口吞吐量、港口区位等适应性指标对港口节点适应度进行度量,构建出一个适应度BA模型;对港口节点度分布的理论推导与数值模拟结果表明:(1)该模型的度分布不仅取决于港口节点的度,更依赖于港口节点的适应度。(2)该模型能够得到一个度分布服从幂律指数为γ=1+1/β的无标度网络。(3)若每个港口节点取相同适应度时,该模型呈现出"先到者赢"的特征;若每个港口节点适应度并不完全相同时,该模型呈现出"适者更富"的特征。