无线传感器网络中无标度拓扑的动态容错性分析

增强网络的动态容错性对于抵御级联失效具有重要的现实意义.根据无线传感器网络无标度拓扑中节点可变负载和恒定容量的特点建立级联失效模型,在随机节点失效下研究负载参数和拓扑参数对其级联失效容错性的影响规律,解析推导出该网络大规模级联失效的承载极限,同时模拟结果发现该网络的级联失效容错性与其度分布系数和幂指数正相关.这为从参数优化角度抵御随机节点失效下无线传感器网络无标度拓扑级联失效危害提供了依据.     

带有层级结构的复杂网络级联失效模型

针对现实世界的网络中普遍存在的层级结构建立一个级联失效模型, 该模型可用于优化金融、物流网络设计. 选择的层级网络模型具有树形骨架和异质的隐含连接, 并且骨架中每层节点拥有的分枝数服从正态分布. 级联失效模型中对底层节点的打击在不完全信息条件下进行, 也即假设打击者无法观察到隐含连接. 失效节点的负载重分配考虑了层级异质性, 它可以选择倾向于向同级或高层级完好节点分配额外负载. 仿真实验表明, 层级网络的拓扑结构随连接参数变化逐渐从小世界网络过渡到随机网络. 网络级联失效规模随隐含连接比例呈现出先增加后降低的规律. 负载重分配越倾向于高层级节点, 网络的抗毁损性越高. 同时, 由于连接参数会改变隐含连接在不同层级之间的分布, 进而对网络的抗毁损性产生显著影响, 为了提高网络抗毁损能力, 设计网络、制定管理控制策略时应合理设定连接参数. 关键词： 复杂网络 级联失效 层级结构     

具有弱依赖组的复杂网络上的级联失效

复杂系统的功能通过节点之间的连接而维持,部分节点的失效会对系统的连通性造成破坏而影响整体的功能.除此之外,部分节点还会通过隐含依赖性而形成“依赖组”,其中一个节点的失效会对依赖组中其余节点造成损害.本文研究了“依赖组”的平均规模,规模分布和节点之间的依赖强度对网络级联失效和鲁棒性的影响.通过数值模拟和理论分析发现,网络的级联失效在尺度层次上可以分为“组内级联”和“组间级联”两个过程.在组内级联过程中,一个节点的失效会通过节点之间的依赖性对组内其他节点造成破坏,从而诱发更多节点的失效,进而产生更大的破坏力.在组间级联过程中,失效节点会引起网络发生破碎而导致组外节点脱离网络巨分支而失效,这就引起了失效节点的跨依赖组传播.在这两种失效过程的共同作用下,网络在级联失效过程中会表现出连续和不连续的两种渗流相变现象,这两种相变现象的发生与节点间的依赖强度、网络度分布以及依赖组规模分布有关.这意味着通过控制依赖组的特征,如依赖组中节点之间的依赖强度或依赖组规模分布,可以避免系统突然崩溃进而提高网络的鲁棒性.     

基于时间序列的网络失效模型

随着网络科学的发展,静态网络已不能清晰刻画网络的动态过程.在现实网络中,个体之间的交互随时间而快速演化.这种网络模式将时间与交互过程紧密联系,能够清晰刻画节点的动态过程.因此,如何更好地基于时间序列刻画网络行为变化是现有级联失效研究的重要问题.为了更好地研究该问题,本文提出一种基于时间序列的失效模型.通过随机攻击某时刻的节点,分析了时间、激活比例、连边数、连接概率4个参数对失效的影响并发现网络相变现象.同时为验证该模型的有效性与科学性,采用真实网络进行研究.实验表明,该模型兼顾时序以及传播动力学,具有较好的可行性,为解释现实动态网络的级联传播提供了参考.     

一种新型二分网络类局域世界演化模型

现实世界中复杂网络的演化存在很明显的局域选择现象,然而目前关于二分网络中的局域世界演化模型研究较少．因此,本文建立了一个基于二分网络的类局域世界演化模型．首先定义了网络节点度值的饱和度．在此基础上提出了一种新型二分网络局域世界演化模型．新节点加入系统不需要全局知识,而是通过节点在网络演化的不同时刻度值饱和度为选择条件构造新节点的局域世界,然后利用择优连接从局域世界中选择节点增加连边完成网络演化．此类模型中新节点的局域世界是通过节点饱和度的限制被动生成,因此又称为类局域世界模型．通过模拟分析发现在节点度值饱和度的限制下择优连接并没有产生具有幂率特性的度分布,而是生成了度分布相对均匀的二分网络,即节点度值分布区间较小．此外,本文还给出了该网络的混合系数计算结果,该结果显示网络同配性与网络参数的选择有关,这一结果与网络邻点平均度的模拟结果一致．     

利用节点效率评估复杂网络功能鲁棒性

为了克服现有复杂网络鲁棒性研究模型只考虑节点失效的局部影响性和网络拓扑鲁棒性的缺陷, 提出了一种利用节点效率来评估复杂网络功能鲁棒性的方法. 该方法综合考虑节点失效的全局影响性, 利用网络中节点的效率来定义各节点的负载、极限负载和失效模型, 通过打击后网络中最终失效节点的比例来衡量网络的功能鲁棒性, 并给出了其评估优化算法. 实验分析表明该方法对考虑节点负载的复杂网络功能鲁棒性的评定可行有效, 对于大型复杂网络可以获得理想的计算能力.     

复杂网络中带有应急恢复机理的级联动力学分析

提出带有应急恢复机理的网络级联故障模型,研究模型在最近邻耦合网络,Erdos-Renyi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网络级联动力学行为.给出了应急恢复机理和网络效率的定义,并研究了模型中各参数对网络效率和网络节点故障率在级联故障过程中变化情况的影响.结果表明,模型中应急恢复概率的增大减缓了网络效率的降低速度和节点故障率的增长速度,并且提高了网络的恢复能力.而且网络中节点负载容量越大,网络效率降低速度和节点故障率的增长速度越慢.同时,随着节点过载故障概率的减小,网络效率的降低速度和节点故障率的增长速度也逐渐减缓.此外,对不同网络拓扑中网络效率和网络节点故障率在级联故障过程中的变化情况进行分析,结果发现网络拓扑节点度分布的异质化程度的增大,提高了级联故障所导致的网络效率的降低速度和网络节点故障率的增长速度.以上结果分析了复杂网络中带有应急恢复机理的网络级联动力学行为,为实际网络中级联故障现象的控制和防范提供了参考.     

多层网络级联失效的预防和恢复策略概述

现实生活中,与国计民生密切相关的基础设施网络大多不是独立存在的,而是彼此之间相互联系或依赖的,于是用于研究这些系统的多层网络模型随之产生.多层网络中的节点在失效或者遭受攻击后会因"层内"和"层间"的相互作用而产生级联效应,从而使得失效能够在网络层内和层间反复传播并使得失效规模逐步放大.因此,多层网络比单个网络更加脆弱.多层网络级联失效产生的影响和损失往往是非常巨大的,所以对多层网络级联失效的预防和恢复的研究具有重大意义.就多层网络级联失效的预防而言,主要包含故障检测,保护重要节点,改变网络耦合机制和节点备份等策略.就多层网络发生级联失效后的恢复策略而言,主要包含共同边界节点恢复、空闲连边恢复、加边恢复、重要节点优先恢复、更改拓扑结构、局域攻击修复、自适应边修复等策略.     

疾病传播与级联失效相互作用的研究:度不相关网络中疾病扩散条件的分析

在网络科学中,对疾病传播和级联失效的研究分属两个独立的领域,但在实际中存在许多两个过程相互耦合的情况. 比如在通信网络中,病毒传播会对数据传输造成影响,导致网络中负载变化,进而可能引发级联失效. 这个现象已被观察到. 通过建立两个动态过程相互作用的模型及针对该模型的分析,本文给出了计入节点的负载和容量时疾病爆发的条件. 这一条件是由描述疾病传播速率的传播概率与描述节点容量大小的冗余系数共同决定的. 进一步探讨表明,当疾病传播速率一定而冗余系数变化时,疾病恰好开始传播的临界点附近未感染且未失效的节点的数量是最大的,即在此点上网络处于最佳工作状态. 因此给出疾病爆发的临界条件具有重要意义. 关键词： 复杂网络 疾病传播 级联失效     

基于在线社交网络的信息传播模型

本文构造了一个基于在线社交网络的信息传播模型.该模型考虑了节点度和传播机理的影响,结合复杂网络和传染病动力学理论,进一步建立了动力学演化方程组.该方程组刻画了不同类型节点随着时间的演化关系,反映了传播动力学过程受到网络拓扑结构和传播机理的影响.本文模拟了在线社交网络中的信息传播过程,并分析了不同类型节点在网络中的行为规律.仿真结果表明:由于在线社交网络的高度连通性,信息在网络中传播的门槛几乎为零;初始传播节点的度越大,信息越容易在网络中迅速传播;中心节点具有较大的社会影响力;具有不同度数的节点在网络中的变 关键词： 在线社交网络 信息传播 微分方程 传染病动力学     

基于可调负载重分配的无标度网络连锁效应分析

为了深入研究复杂网络抵制连锁故障的全局鲁棒性,针对现实网络上的负载重分配规则常常是介于全局分配与最近邻分配、均匀分配与非均匀分配的特点,围绕负荷这一影响连锁故障发生和传播最重要的物理量以及节点崩溃后的动力学过程,提出了一种可调负载重分配范围与负载重分配异质性的复杂网络连锁故障模型,并分析了该模型在无标度网络上的连锁故障条件.数值模拟获得了复杂网络抵制连锁故障的鲁棒性与模型中参数的关系.此外,基于网络负载分配规则的分析以及理论解析的推导,验证了数值模拟结论,也证明在最近邻与全局分配两种规则下都存在负载分配均匀性参数等于初始负荷强度参数即β=τ使得网络抵御连锁故障的能力最强.     

基于负荷局域择优重新分配原则的复杂网络上的相继故障

相继故障普遍存在现实的网络系统中,为了更好地探讨复杂网络抵制相继故障的全局鲁棒性,采用网络中节点j上的初始负荷为L_j=k^α_j（k_j为节点j的度）的形式,并基于崩溃节点上负荷的局域择优重新分配的原则,提出了一个新的相继故障模型.依据新的度量网络鲁棒性的指标,探讨了4种典型复杂网络上的相继故障现象.数值模拟表明, 关键词： 相继故障 复杂网络 关键阈值 相变     

Load-redistribution strategy based on time-varying load against cascading failure of complex network

Cascading failure can cause great damage to complex networks, so it is of great significance to improve the network robustness against cascading failure. Many previous existing works on load-redistribution strategies require global information, which is not suitable for large scale networks, and some strategies based on local information assume that the load of a node is always its initial load before the network is attacked, and the load of the failure node is redistributed to its neighbors according to their initial load or initial residual capacity. This paper proposes a new load-redistribution strategy based on local information considering an ever-changing load. It redistributes the loads of the failure node to its nearest neighbors according to their current residual capacity, which makes full use of the residual capacity of the network. Experiments are conducted on two typical networks and two real networks, and the experimental results show that the new load-redistribution strategy can reduce the size of cascading failure efficiently.     

Cascading failure in the wireless sensor scale-free networks

In the practical wireless sensor networks(WSNs), the cascading failure caused by a failure node has serious impact on the network performance. In this paper, we deeply research the cascading failure of scale-free topology in WSNs. Firstly,a cascading failure model for scale-free topology in WSNs is studied. Through analyzing the influence of the node load on cascading failure, the critical load triggering large-scale cascading failure is obtained. Then based on the critical load,a control method for cascading failure is presented. In addition, the simulation experiments are performed to validate the effectiveness of the control method. The results show that the control method can effectively prevent cascading failure.     

A decentralized flow redistribution algorithm for avoiding cascaded failures in complex networks

A decentralized random algorithm for flow distribution in complex networks is proposed. The aim is to maintain the maximum flow while satisfying the flow limits of the nodes and links in the network. The algorithm is also used for flow redistribution after a failure in (or attack on) a complex network to avoid a cascaded failure while maintaining the maximum flow in the network. The proposed algorithm is based only on the information about the closest neighbours of each node. A mathematically rigorous proof of convergence with probability 1 of the proposed algorithm is provided.     

Complex networks: Dynamics and security

This paper presents a perspective in the study of complex networks by focusing on how dynamics may affect network security under attacks. In particular, we review two related problems: attack-induced cascading breakdown and range-based attacks on links. A cascade in a network means the failure of a substantial fraction of the entire network in a cascading manner, which can be induced by the failure of or attacks on only a few nodes. These have been reported for the internet and for the power grid (e.g., the August 10, 1996 failure of the western United States power grid). We study a mechanism for cascades in complex networks by constructing a model incorporating the flows of information and physical quantities in the network. Using this model we can also show that the cascading phenomenon can be understood as a phase transition in terms of the key parameter characterizing the node capacity. For a parameter value below the phase-transition point, cascading failures can cause the network to disintegrate almost entirely. We will show how to obtain a theoretical estimate for the phase-transition point. The second problem is motivated by the fact that most existing works on the security of complex networks consider attacks on nodes rather than on links. We address attacks on links. Our investigation leads to the finding that many scale-free networks are more sensitive to attacks on short-range than on long-range links. Considering that the small-world phenomenon in complex networks has been identified as being due to the presence of long-range links, i.e., links connecting nodes that would otherwise be separated by a long node-to-node distance, our result, besides its importance concerning network efficiency and security, has the striking implication that the small-world property of scale-free networks is mainly due to short-range links.     

Cascading failure in multilayer networks with dynamic dependency groups

The cascading failure often occurs in real networks. It is significant to analyze the cascading failure in the complex network research. The dependency relation can change over time. Therefore, in this study, we investigate the cascading failure in multilayer networks with dynamic dependency groups. We construct a model considering the recovery mechanism.In our model, two effects between layers are defined. Under Effect 1, the dependent nodes in other layers will be disabled as long as one node does not belong to the largest connected component in one layer. Under Effect 2, the dependent nodes in other layers will recover when one node belongs to the largest connected component. The theoretical solution of the largest component is deduced and the simulation results verify our theoretical solution. In the simulation, we analyze the influence factors of the network robustness, including the fraction of dependent nodes and the group size, in our model. It shows that increasing the fraction of dependent nodes and the group size will enhance the network robustness under Effect 1. On the contrary, these will reduce the network robustness under Effect 2. Meanwhile, we find that the tightness of the network connection will affect the robustness of networks. Furthermore, setting the average degree of network as 8 is enough to keep the network robust.     

Robustness analysis of network controllability

Structural controllability, which is an interesting property of complex networks, attracts many researchers from various fields. The maximum matching algorithm was recently applied to explore the minimum number of driver nodes, where control signals are injected, for controlling the whole network. Here we study the controllability of directed Erdös–Rényi and scale-free networks under attacks and cascading failures. Results show that degree-based attacks are more efficient than random attacks on network structural controllability. Cascade failures also do great harm to network controllability even if they are triggered by a local node failure.