基于元胞自动机考虑传播延迟的复杂网络病毒传播研究

基于元胞自动机,研究传播延迟对复杂网络病毒传播动力学行为的影响,提出一种新的易染状态-感染状态-易染状态(SIS)传播模型.研究表明,传播延迟的存在显著降低了网络的传播临界值,增强了网络中病毒爆发的危险性.研究还发现,随着传播延迟的增大,病毒的感染程度以及传播速率都明显增大.此外,SIS传播模型不仅能够反映病毒的平均传播趋势,而且可以描述病毒随时间的动态演化过程以及病毒的爆发和消亡等概率事件,从而有效地克服了利用平均场方法构建的微分方程模型只能反映病毒平均传播趋势的局限性.同时,还给出有效控制网络中病毒传 关键词： 复杂网络 病毒传播 元胞自动机 传播延迟     

节点抗攻击存在差异的无尺度网络恶意软件传播研究

在考虑节点抗攻击能力存在差异情形下,研究了恶意软件在无尺度网络中的传播行为.基于元胞自动机理论,建立了节点具有攻击差异的恶意软件传播模型.通过定义脆弱性函数,以描述不同度节点的抗攻击差异,使得模型更具普遍性.研究了不同形式的脆弱性函数对恶意软件在无尺度网络中的传播临界值和时间演化的影响.研究表明,节点抗攻击能力的差异对传播行为会产生重要影响,如导致传播临界值改变、传播速度减缓.研究指出,脆弱性函数是网络选择适合的免疫策略的重要依据.     

考虑行车状态的一维元胞自动机交通流模型

在Nagel Schrekenberg单车道元胞自动机交通流模型（简称NS模型）的基础上，考虑车辆之间的相对运动薛郁等提出了一种改进的单车道元胞自动机交通流模型（简称改进的NS模型）.通过两种情况列出了改进的NS模型存在不尽周严的地方，随之在新模型中引入了行车状态 变量和反馈规则，从而控制车辆出现倒车和刹车过急等现象.通过计算机对新模型进行模拟 ，发现减速概率和车流密度对车流状态的演化影响很大，当减速概率高（如道路条件差）时 ，即使车流密度低，车流也会出现局部堵塞状态；而当减速概率一定时，随着车流密度增加 ，车流的运动相与堵塞相发生了全局性的交替出现，此时类似于波的波峰和波谷的传播.与 改进的NS模型相比较，新模型模拟的车流量较高，说明新模型减少了车流的总体停滞状态. 关键词： 交通流 元胞自动机 行车状态 反馈规则     

考虑驾驶方式改变的一维元胞自动机交通流模型

在一维元胞自动机交通流WWH模型和SDNS模型的基础上,建立了考虑驾驶方式改变的元胞自动机模型（Change-CA模型）.具体描述为驾驶员可根据交通环境选择不同的驾驶方式在道路上驾车行驶,以各自的演化规则进行状态更新,同时定义了驾驶方式更新原则.通过计算机数值模拟,发现驾驶方式可变时,模型模拟得到的混合交通流流量较大;保守型驾驶方式对交通流变化的影响随改变概率增大而减少.并且在演化过程中,驾驶方式改变频率的变化趋势与改变概率、安全概率密切相关.与NS模型和SDNS模型相比,Change-CA模型减少了车流 关键词： 交通流 元胞自动机 驾驶方式 计算机数值模拟     

一种基于元胞自动机的自适应网络病毒传播模型

自适应网络是节点动力学和网络动力学相互作用和反馈的演化网络. 基于元胞自动机建立自适应网络中易感-感染-易感(susceptible-infected-susceptible)的病毒传播模型,研究节点为了规避病毒传播所采取的多种网络重连规则对病毒传播及网络统计特征的影响. 结果表明:自适应网络中的重连规则可以有效减缓病毒传播速度,降低病毒传播规模;随机重连规则使得网络统计特征趋于随机网络;基于元胞自动机建立的传播模型清晰地表达了病毒在传播过程中的双稳态现象. 关键词： 自适应网络 传播动力学 网络动力学 元胞自动机     

基于元胞自动机的复杂信息系统安全风险传播研究

基于元胞自动机建立复杂信息系统安全风险传播模型, 研究复杂信息系统安全风险在最近邻耦合网络、 随机网络,　Watts-Strogatz 小世界网络和Barabasi-Albert无标度网络 四种网络拓扑下的传播问题. 通过研究安全风险传播模型在四种网络拓扑下安全风险的传播阈值, 与现有的传播阈值研究成果进行比较, 验证模型的正确性, 并分析验证网络拓扑结构中度分布的异质化程度越高传播阈值越小的结论. 通过对安全风险的传播演化趋势进行研究, 分析验证网络度分布的异质化程度越高、安全风险影响范围越小、传播速度越快的结论, 并指出度分布的异质化程度越高、模型后期的免疫机制对控制安全风险传播的效果越缓慢. 通过对安全风险在传播最早期就趋于消亡的情况进行研究, 分析得出安全风险在传播之初就趋于消亡的消亡率与传播率之间呈现近似负指数的关系, 并且初期的感染源越多安全风险的消亡率越低. 分析了影响复杂信息系统安全风险传播的关键要素, 对复杂信息系统中安全风险传播的控制具有指导作用. 关键词： 复杂信息系统 复杂网络 安全风险传播 元胞自动机     

基于元胞自动机的无线传感网络整体行为研究

探讨自组织通信网络中局部行为与系统整体行为的关联, 对于相关系统的设计和控制具有重要应用价值. 利用二维元胞自动机模型对无线传感网络的拓扑控制过程进行模拟, 可以分析节点间局部交互作用规则对网络整体行为的影响. 研究表明, 在不同的局部演化规则作用下, 该系统呈现出复杂的时空演化现象, 发现系统整体行为空间中存在振荡、衰减、稳定等基本模式, 并且该模型可以反映系统内不同全局目标之间的折中关系, 为进一步研究无线传感网络整体行为的控制问题提供一种新的途径. 关键词： 自组织 元胞自动机 通信网络 无线传感网络     

高速桥梁瓶颈模型属性的研究

针对现实中桥梁形成的交通流瓶颈，建立了一个基于元胞自动机交通流桥梁瓶颈模型. 在周期性边界条件下，将系统的状态分成六“相”，利用平均场理论和决定性NS模型的结果，得到每条车道上的密度关系. 分析的理论值与计算机数值模拟结果符合得很好. 关键词： 元胞自动机模型 桥梁瓶颈 决定性NS模型 选择概率     

基于元胞自动机法的高温金属腐蚀行为模拟

本文基于元胞自动机方法对镍-铬合金在高温的含硫腐蚀性气氛中的氧化腐蚀行为进行模拟。模型中的重要变量包括反应概率、反应时间以及腐蚀层厚度。模拟结果表明,元胞自动机方法可以模拟出金属在氧化-硫化复杂化学反应体系,腐蚀层厚度符合Wagner抛物线理论。另外,研究了不同参数对镍-铬二元合金腐蚀的影响,结果表明,相同反应概率下,Cr浓度升高,反应物厚度减小,体现了铬的腐蚀防护性。     

网络条件下列车追踪模型及延迟传播的研究

描述了一种移动闭塞下考虑不同类型列车混跑时基于元胞自动机的列车追踪模型.将该模型应用到铁路网，模拟了网络条件下列车运行情况.应用该模型模拟了移动闭塞下列车延迟传播的现象.分析了发车间隔松弛时间、初始延迟时间等因素对列车延迟的影响.将该模型的模拟结果和理论公式的计算结果进行了比较，验证了模型的可靠性和有效性. 关键词： 元胞自动机 铁路网络 列车追踪模型 移动闭塞     

Constructing refined null models for statistical analysis of signed networks

The establishment of effective null models can provide reference networks to accurately describe statistical properties of real-life signed networks.At present,two classical null models of signed networks(i.e.,sign and full-edge randomized models)shuffle both positive and negative topologies at the same time,so it is difficult to distinguish the effect on network topology of positive edges,negative edges,and the correlation between them.In this study,we construct three re-fined edge-randomized null models by only randomizing link relationships without changing positive and negative degree distributions.The results of nontrivial statistical indicators of signed networks,such as average degree connectivity and clustering coefficient,show that the position of positive edges has a stronger effect on positive-edge topology,while the signs of negative edges have a greater influence on negative-edge topology.For some specific statistics(e.g.,embeddedness),the results indicate that the proposed null models can more accurately describe real-life networks compared with the two existing ones,which can be selected to facilitate a better understanding of complex structures,functions,and dynamical behaviors on signed networks.     

Emergence of fractal scale-free networks from stochastic evolution on the Cayley tree

An unexpected recognition of fractal topology in some real-world scale-free networks has evoked again an interest in the mechanisms stimulating their evolution. To explain this phenomenon a few models of a deterministic construction as well as a probabilistic growth controlled by a tunable parameter have been proposed so far. A quite different approach based on the fully stochastic evolution of the fractal scale-free networks presented in this Letter counterpoises these former ideas. It is argued that the diffusive evolution of the network on the Cayley tree shapes its fractality, self-similarity and the branching number criticality without any control parameter. The last attribute of the scale-free network is an intrinsic property of the skeleton, a special type of spanning tree which determines its fractality.     

Model for cascading failures with adaptive defense in complex networks

<正>This paper investigates cascading failures in networks by considering interplay between the flow dynamic and the network topology,where the fluxes exchanged between a pair of nodes can be adaptively adjusted depending on the changes of the shortest path lengths between them.The simulations on both an artificially created scale-free network and the real network structure of the power grid reveal that the adaptive adjustment of the fluxes can drastically enhance the robustness of complex networks against cascading failures.Particularly,there exists an optimal region where the propagation of the cascade is significantly suppressed and the fluxes supported by the network are maximal. With this understanding,a costless strategy of defense for preventing cascade breakdown is proposed.It is shown to be more effective for suppressing the propagation of the cascade than the recent proposed strategy of defense based on the intentional removal of nodes.     

A temporal ant colony optimization approach to the shortest path problem in dynamic scale-free networks

A large number of networks in the real world have a scale-free structure, and the parameters of the networks change stochastically with time. Searching for the shortest paths in a scale-free dynamic and stochastic network is not only necessary for the estimation of the statistical characteristics such as the average shortest path length of the network, but also challenges the traditional concepts related to the “shortest path” of a network and the design of path searching strategies. In this paper, the concept of shortest path is defined on the basis of a scale-free dynamic and stochastic network model, and a temporal ant colony optimization (TACO) algorithm is proposed for searching for the shortest paths in the network. The convergence and the setup for some important parameters of the TACO algorithm are discussed through theoretical analysis and computer simulations, validating the effectiveness of the proposed algorithm.     

Weighted coupling for geographical networks: Application to reducing consensus time in sensor networks

Although many complex real-world networks are weighted, unweighted networks are used in many applications such as sensor networks. In this Letter it is shown using properly weighted networks the performance can be greatly enhanced by reducing the time necessary for the average consensus. Random geographical models are adapted as network models and a method based on mutually coupled phase oscillators is used for providing average consensus over the network. The consensus time is calculated by numerically solving the network's differential equations and monitoring the average error. The simulation results on some sample networks show that the consensus time is dramatically reduced when the proposed weights are used for the links of the underlying network.     

Deterministic scale-free small-world networks of arbitrary order

In many real-life networks, both the scale-free distribution of degree and small-world behavior are important features. There are many random or deterministic models of networks to simulate these features separately. However, there are few models that combine the scale-free effect and small-world behavior, especially in terms of deterministic versions. What is more, all the existing deterministic algorithms running in the iterative mode generate networks with only several discrete numbers of nodes. This contradicts the purpose of creating a deterministic network model on which we can simulate some dynamical processes as widely as possible. According to these facts, this paper proposes a deterministic network generation algorithm, which can not only generate deterministic networks following a scale-free distribution of degree and small-world behavior, but also produce networks with arbitrary number of nodes. Our scheme is based on a complete binary tree, and each newly generated leaf node is further linked to its full brother and one of its direct ancestors. Analytical computation and simulation results show that the average degree of such a proposed network is less than 5, the average clustering coefficient is high (larger than 0.5, even for a network of size 2 million) and the average shortest path length increases much more slowly than logarithmic growth for the majority of small-world network models.     

Synchronizability of small-world networks generated from ring networks with equal-distance edge additions

This paper investigates the impact of edge-adding number m and edge-adding distance d on both synchronizability and average path length of NW small-world networks generated from ring networks via random edge-adding. It is found that the synchronizability of the network as a function of the distance d is fluctuant and there exist some d that have almost no impact on the synchronizability and may only scarcely shorten the average path length of the network. Numerical simulations on a network of Lorenz oscillators confirm the above results. This phenomenon shows that the contributions of randomly added edges to both the synchronizability and the average path length are not uniform nor monotone in building an NW small-world network with equal-distance edge additions, implying that only if appropriately adding edges when building up the NW small-word network can help enhance the synchronizability and/or reduce the average path length of the resultant network. Finally, it is shown that this NW small-world network has worse synchronizability and longer average path length, when compared with the conventional NW small-world network, with random-distance edge additions. This may be due to the fact that with equal-distance edge additions, there is only one shortcut distance for better information exchange among nodes and for shortening the average path length, while with random-distance edge additions, there exist many different distances for doing so.