基于级联失效的复杂网络抗毁性

传统的复杂网络抗毁性研究主要基于网络静态连通性,而忽视了网络动态特征。该文在网络动态性基础上,研究级联失效条件下复杂网络的抗毁性能,对ER随机网络模型、BA无标度网络模型和PFP互联网拓扑模型这三种模型在不同攻击策略下的抗毁性进行了对比分析和仿真实验。实验结果表明：在随机攻击下,ER网络表现最为脆弱,而BA网络的抗毁性...     

Exploring complex networks

The study of networks pervades all of science, from neurobiology to statistical physics. The most basic issues are structural: how does one characterize the wiring diagram of a food web or the Internet or the metabolic network of the bacterium Escherichia coli? Are there any unifying principles underlying their topology? From the perspective of nonlinear dynamics, we would also like to understand how an enormous network of interacting dynamical systems-be they neurons, power stations or lasers-will behave collectively, given their individual dynamics and coupling architecture. Researchers are only now beginning to unravel the structure and dynamics of complex networks.     

Self-similarity of complex networks

Complex networks have been studied extensively owing to their relevance to many real systems such as the world-wide web, the Internet, energy landscapes and biological and social networks. A large number of real networks are referred to as 'scale-free' because they show a power-law distribution of the number of links per node. However, it is widely believed that complex networks are not invariant or self-similar under a length-scale transformation. This conclusion originates from the 'small-world' property of these networks, which implies that the number of nodes increases exponentially with the 'diameter' of the network, rather than the power-law relation expected for a self-similar structure. Here we analyse a variety of real complex networks and find that, on the contrary, they consist of self-repeating patterns on all length scales. This result is achieved by the application of a renormalization procedure that coarse-grains the system into boxes containing nodes within a given 'size'. We identify a power-law relation between the number of boxes needed to cover the network and the size of the box, defining a finite self-similar exponent. These fundamental properties help to explain the scale-free nature of complex networks and suggest a common self-organization dynamics.     

Controllability of complex networks

The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system's entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network's degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes.     

多层前馈神经网络的误差特性分析

为分析人工神经网络（ＡＮＮ）的品质或性能对权值扰动及输入误差的容忍特性,文章基于输入及权值的随机模型,采用统计学的方法,得到了由ｓｉｇｍｏｉｄ型神经元构成的任意多层前馈神经网络在任意大小且具有任意相关性的输入误差与／或权值扰动下,输出误差特性的通用算法。仿真及对比理论计算结果表明了所提算法是正确的。     

神经网络BP算法的误差分级迭代法

本文结合某一工程实例，对BP算法进行了改进，提出了误差分级迭代法．通过实例分析，该方法确能提高收敛速度，克服初始权值的影响，同时，学习样本次序对其影响也不大．因此，该方法能有效地改善BP网络的性能．最后，对误差分级迭代法的工作机理进行了分析．     

基于复杂网络的信号检测与传递

近年来,关于复杂网络的研究已取得了长足的进展,且将复杂网络理论应用于其他学科的研究正方兴未艾.本文简要介绍复杂网络理论在信号检测与传递方面的一些初步研究进展,主要关注三方面的研究成果:(1)复杂网络上的信号放大;(2)与复杂网络有关的信号检测;(3)复杂网络上的自维持振荡.这些阶段性的研究成果从复杂网络的新角度加深了我们对神经元网络宏观动力学行为的微观机制的理解,并有助于刻画信息传递从神经元物理网络向脑功能网络的过渡.     

复杂网络抗毁性研究进展

随着复杂网络研究的兴起,复杂网络抗毁性研究的重大理论意义和应用价值日益突显出来,成为极其重要而且富有挑战性的前沿科研课题.本文总结综述了国防科技大学信息系统与管理学院在复杂网络抗毁性领域取得的研究进展,具体包括:研究了不完全信息条件下复杂网络拓扑结构抗毁性;提出了复杂网络拓扑结构抗毁性的谱测度方法;分析了3种结构属性对复杂网络拓扑结构抗毁性的影响;提出了基于禁忌搜索的复杂网络拓扑结构抗毁性仿真优化方法.     

SDN环境下基于KNN的DDoS攻击检测方法

DDo S攻击是当前互联网面临的主要威胁之一,如何快速准确地检测DDo S攻击是网络安全领域研究的热点问题。文中提出了一种在SDN环境下基于KNN算法的模块化DDo S攻击检测方法,该方法选取SDN网络的5个关键流量特征,采用优化的KNN算法对选取的流量特征进行流量异常检测,最后基于NOX控制器和Net FPGA交换机进行了实验验证。实验结果表明:相对其他的分类检测算法,所提的检测方案具有更高的识别率和更低的误报率。     

复杂网络节点中心性

将网络中心性方法按照理论特征划分为节点关联性、网络最短路和模拟流问题,并对现实网络的局域性、信息完备性和动态性进行了深入分析,在此基础上建立了中心性方法与实际网络之间的匹配关系.     

Functional cartography of complex metabolic networks

High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that we can find functional modules in complex networks, and classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a 'cartographic representation' of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with most potential for immediate applicability. We use our method to analyse the metabolic networks of twelve organisms from three different superkingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that metabolites that participate in only a few reactions but that connect different modules are more conserved than hubs whose links are mostly within a single module.     

图与复杂网络的拉普拉斯谱(英文)

总结了图与复杂网络(包括随机图与小世界网络)的拉普拉斯谱的最新的结果和研究进展.主要内容包括给定度序列的拉普拉斯谱半径、拉普拉斯系数、代数连通度、双随机矩阵和随机图与小世界网络的谱的性质.并且提出了可能进一步研究的一些相关的问题.     

有向复杂网络的Poisson模型

考虑了节点到达过程是Poisson过程的有向复杂网络.本文研究了这类网络的瞬态度分布和稳态平均度分布.利用Poisson过程理论对这类网络进行了分析,获得了度分布的解析表达式.结果表明,虽然这类网络的稳态平均入度和稳态平均出度分布与节点的到达过程无关,但瞬态入度和出度分布依赖于节点的到达过程.     

复杂网络同步时间可控的投影同步

基于同步时间可控的投影同步方法研究了复杂网络混沌系统的有限时间同步问题,根据有限时间稳定性理论设计了控制器,能够使驱动网络与响应网络达到有限时间同步,同步误差按预设的指数速率收敛.数值算例说明了方法的有效性.     

DPSK和NRZ调制信号对透明光网络中的大功率串扰攻击容忍度研究

透明光网络受到越来越多的关注,已成为未来网络发展的一大趋势.然而,网络的透明性也带来了光层攻击的隐患,例如大功率的攻击信号可以利用带内串扰效应对网络实施攻击行为.由于差分移相键控(differential phase-shift-keying,DPSK)对带内串扰具有较好的容忍度,因此对它能否抵抗大功率带内串扰攻击进行了探讨.通过在VPI仿真软件上搭建光通信系统,并检测信号的BER,发现DPSK对带内串扰攻击的容忍度比不归零码(non-return-zero,NRZ)高9 dB,可见其具有更强的抗攻击能力.     

超立方体网络容错性的概率分析

用概率分析的方法研究在给定结点错误概率的情况下超立方体网络容错性的概率,证明了一个具有1024个结点的10维超立方体网络能够容许多达10%的错误结点而具有99％的概率确保正确结点的连通性;如果结点的错误概率不超过0.1％,则所有实际规模的超立方体网络(结点数可多达1万亿个)能够具有99.9％的概率确保正确结点的连通性.研究结果表明,所提出的方法也能够用于研究其他层次结构的网络和其他网络通信问题.     

软件定义网络中基于贝叶斯ARTMAP的DDoS攻击检测模型

为解决SDN(software defined network,软件定义网络)架构下DDoS(distributed denial of service,分布式拒绝服务)攻击检测问题,提出基于贝叶斯ARTMAP的DDoS攻击检测模型. 流量统计模块主要收集捕获到的流表信息,特征提取模块提取流表中的关键信息并获取关键特征,分类检测模块通过贝叶斯ARTMAP提取分类规则,并通过粒子群算法对参数进行优化,对新的数据集进行分类检测.仿真实验证明了模型所提取的5元特征的有效性,并且该模型与3种传统的DDoS攻击检测模型相比检测成功率提高了0.96%~3.71%,误警率降低了0.67%~2.92%.     

复杂网络的社团结构发现

社团结构是复杂网络的一个重要拓扑特征,社团结构发现是研究复杂网络的一个基础性问题,近十年来得到了广泛的关注。本文概要了非重叠社团发现的典型算法,较全面地归纳分析了重叠社团发现算法。并指出了社团发现研究尚存在的一些问题和进一步的研究方向。     

中心方法在复杂网络中的比较

在复杂网络分析中,中心性分析已经显示出是一种很有价值的方法。它用来检测网络中的关键点以及对网络元素进行排序。为了支撑这种分析,文中讨论了5种中心性方法,并且在一个人工网络和2个实际网络中展示了它们的应用。这些方法的运用显示了在某种网络中有某种较强的关联,但在另一种网络中有较弱的关联。分析表明:对于复杂网络分析,几种方法应当同时考虑。