考虑权值减少的局域世界网络模型上的病毒传播研究

深入分析了局域加权网络的演化机制,建立了一个新的局域世界加权网络模型。对网络结构变化对病毒在网络上的传播行为进行了研究,发现网络中病毒传播行为和网络拓扑结构的参数变化存在密切关系。     

自适应复杂网络中的病毒传播模型

提出了一个基于自适应复杂网络的病毒传播模型。模型中,易感节点为了不被感染,能够有意识地避开与感染节点的连接,此过程一方面使得网络结构发生了变化,另一方面网络结构的变化又反过来对病毒传播过程造成了影响。着重考查了模型中个体的躲避行为对病毒传播效果的影响,结果显示,在个体躲避行为的驱动下,系统的最终染病节点数会发生振荡,并且在一定的参数范围内系统出现了双稳状态。     

无标度网络中基于最短路径免疫策略的病毒传播研究

传统病毒免疫策略大多基于网络的全局拓扑信息。然而现实生活中的大部分复杂网络仅仅只能了解其局部 拓扑信息。鉴于许多实际复杂网络具有无标度特性,研究了在无标度复杂演化网络中基于网络局部拓扑信息最短路 径免疫策略的病毒传播现象。利用平均场理论建立含个体抵杭力重要因素的无标度网络病毒传播模型,并引入基于 最短路径的免疫策略。比较了随机免疫、目标免疫和最短路径免疫3种策略对无标度复杂网络病毒传播的影响,结果 表明了基于最短路径免疫策略的有效性。     

网络病毒传播模型与分析

互联网是一个不断生长与消亡的具有小世界与无标度特性的网络。基于此,在聚类系数可变的无标度网络上建立病毒传播模型。研究节点消亡速度、网络平均度、计算机连接度对病毒传播的影响。实验结果表明:节点的消亡速度越快,越能减缓病毒的爆发速度;网络的平均度越大,病毒传播越快;病毒爆发常发生在连接度较高的计算机上。这些结论对于防范病毒在互联网上的传播,具有重要的现实意义。     

一种基于复杂网络特性的Internet拓扑模型

文章从复杂网络研究的角度出发,根据Internet的统计特征及其形成机制提出了一种基于消息传递的自组织Internet拓扑模型。该拓扑模型动态模拟整个Internet的生长过程:平面上随机分布的孤立节点通过相互发送消息,消息中保存消息源的优先度等信息,每个节点根据接收到的消息决定如何建立连接。网络由初始的孤立节点自下而上自组织形成一个具有层次结构的Internet拓扑结构。仿真试验表明由该模型生成的拓扑结构在度分布以及聚集系数等方面能够准确地吻合现实Internet拓扑结构。     

自组织网络病毒传播模型研究

针对现有的网络病毒传播模型不适应自组织网络的问题,以无线传感器网络作为自组织网络的背景,提出一种复杂环境下的自组织网络病毒传播模型.该模型引入描述节点通信窗口开放情况参数以模拟休眠机制;引入描述网络链路好坏的参数以模拟环境影响因素;针对传感器节点资源有限等特征,提出一种抑制病毒传播的方案;使用在网络节点中注入补丁包的方...     

应急物流网络中物资短缺传播模型及应用

针对应急物流决策中如何描述物资短缺现象在复杂物流网络中传播的问题, 提出了物资短缺在应急物流网络中的传播模型与算法。该方法基于复杂网络的病毒传播机理, 构建了引入时间参数的物资短缺SI-SIRS传播模型, 并将其应用于包含61个供应点的无标度应急物流网络中物资短缺的传播特性分析。案例分析表明, SI-SIRS模型有效地刻画了物资短缺现象在应急物流网络中的传播行为; 物资短缺现象的传播特性受本身扩散特性、应急物流网络拓扑结构、物资供应点个体差异以及应急物流响应机制的制约。     

多局域世界复杂网络中的病毒传播研究

对多局域世界(MLW)演化模型的构造算法进行改进,以元胞自动机(CA)为工具,研究MLW复杂网络中的病毒传播特性。结果表明,CA能较好反映病毒传播过程中的概率事件和个体之间的交互行为,MLW复杂网络的传播临界值与传染率、病毒爆发率有关,初始感染源的选择对病毒的爆发有重要作用,病毒的传播和消亡速度与传染率以及攻击模式有关。     

计算机网络病毒传播模型SIRH

计算机网络病毒传播模型是研究计算机网络病毒的手段和工具。SIR模型是仿照生物流行病传播机制而建立的病毒传播模型。本文从计算机网络病毒传播的实际情况出发,通过分析SIR模型的不足,提出了一种在计算机网络中具有病毒防范、病毒免疫措施的网络病毒传播模型。SIRH考虑了网络病毒重复感染这种人为因素对网络病毒传播的影响。仿真实验验证了SIRH的有效合理性。     

基于病毒传播网络的基因序列表示学习

基因序列数据中往往存在大量的非编码和缺失序列,现有的基因序列表示大多通过人工方法对高维的基因序列进行特征提取,不仅非常耗时且成功的预测很大程度依赖于生物学知识的正确利用.基于病毒传播网络构建了一种基于图上下文信息的基因序列表示方法,对目标节点病毒序列进行编码后,使用注意力机制对其邻居节点的序列信息进行聚合,从而得到目标节点病毒序列的新的低维表示.进而依据病毒传播网络中相邻节点的基因序列相似性高于不相邻节点的特征,对基因序列表示模型进行优化,训练后得到的新的表示不仅可以有效表达基因序列的特征,同时极大地降低了序列的维度,提高了计算效率.分别在仿真病毒传播网络、新型冠状病毒和艾滋病毒传播网络数据上训练基因序列表示模型,并在相应的网络上进行未采样感染者发现任务.实验结果充分验证了模型的有效性,与其他方法的比较证明了模型的高效性,模型可以有效地在病毒传播网络上发现未采样感染者,这在流行病调查领域也具有一定的实际意义.     

加权局域网络上的病毒传播行为研究

病毒传播问题的研究一直是国际上科学家所关注的焦点,但是在加权局域网络中的病毒传播研究却是空白。由于实际存在的网络很大一部分是加权局域网络,因此研究了一种特定加权局域网络中的传播行为。采用病毒传播的SI模型,令病毒的传播速度和网络的连接权重正相关。对加权局域网络中病毒传播行为的研究表明：加权局域网络的无标度性质和加权局域世界性质对病毒的传播有深刻的影响。由于加权局域网络能够很好地反应实际世界,因此该研究具有很广的应用背景。     

SIS model of epidemic spreading on dynamical networks with community

We present a new epidemic Susceptible-Infected-Susceptible (SIS) model to investigate the spreading behavior on networks with dynamical topology and community structure. Individuals in themodel are mobile agentswho are allowed to perform the inter-community (i.e., long-range) motion with the probability p. The mean-field theory is utilized to derive the critical threshold (λ _C ) of epidemic spreading inside separate communities and the influence of the long-range motion on the epidemic spreading. The results indicate that λ _C is only related with the population density within the community, and the long-range motion will make the original disease-free community become the endemic state. Large-scale numerical simulations also demonstrate the theoretical approximations based on our new epidemic model. The current model and analysis will help us to further understand the propagation behavior of real epidemics taking place on social networks.     

SIS model of epidemic spreading on dynamical networks with community

We present a new epidemic Susceptible-Infected-Susceptible (SIS) model to investigate the spreading behavior on networks with dynamical topology and community structure. Individuals in themodel are mobile agentswho are allowed to perform the inter-community (i.e., long-range) motion with the probability p. The mean-field theory is utilized to derive the critical threshold (λ_C) of epidemic spreading inside separate communities and the influence of the long-range motion on the epidemic spreading. The results indicate that λ_C is only related with the population density within the community, and the long-range motion will make the original disease-free community become the endemic state. Large-scale numerical simulations also demonstrate the theoretical approximations based on our new epidemic model. The current model and analysis will help us to further understand the propagation behavior of real epidemics taking place on social networks.     

Bifurcation and control of disease spreading networks model with two delays

In this paper, the dynamical behaviors are investigated for a complex network with two independent delays. Instead of taking time delays as bifurcation parameters, we choose probability $p$ and parameter $\mu$ as the control parameters to study their effects on local stability and Hopf bifurcation, respectively. Moreover, the conditions for generating Hopf bifurcation are given. Furthermore, we further discuss the effects of two time delays on the critical values of parameters $p$ and $\mu$. Finally, numerical simulations are used to illustrate the validity of the obtained results.     

An alternative approach to characterize the topology of complex networks and its application in epidemic spreading

Based on the mean-field approach, epidemic spreading has been well studied. However, the mean-field approach cannot show the detailed contagion process, which is important in the control of epidemic. To fill this gap, we present a novel approach to study how the topological structure of complex network influences the concrete process of epidemic spreading. After transforming the network structure into hierarchical layers, we introduce a set of new parameters, i.e., the average fractions of degree for outgoing, ingoing, and remaining in the same layer, to describe the infection process. We find that this set of parameters are closely related to the degree distribution and the clustering coefficient but are more convenient than them in describing the process of epidemic spreading. Moreover, we find that the networks with exponential distribution have slower spreading speed than the networks with power-law degree distribution. Numerical simulations have confirmed the theoretical predictions.     

一种用于电子邮件网络中的综合利用网络拓扑与传播参数的免疫方法设计*

电子邮件网络中的传播型攻击是非常严重的网络安全问题。研究界提出了很多种网络免疫方法来解决这个问题,基于节点介数(node betweenness,NB)的方法是目前最好的方法。综合利用电子邮件网络的网络拓扑与传播型攻击的传播参数设计了一种网络免疫方法。在生成的电子邮件网络拓扑模型以及Enron电子邮件网络真实拓扑数据的仿真表明,该方法比NB方法更有效。在某些仿真场景下,本免疫方法能够比NB方法达到50%的改进。     

An alternative approach to characterize the topology of complex networks and its application in epidemic spreading

Based on the mean-field approach, epidemic spreading has been well studied. However, the mean-field approach cannot show the detailed contagion process, which is important in the control of epidemic. To fill this gap, we present a novel approach to study how the topological structure of complex network influences the concrete process of epidemic spreading. After transforming the network structure into hierarchical layers, we introduce a set of new parameters, i.e., the average fractions of degree for outgoing, ingoing, and remaining in the same layer, to describe the infection process. We find that this set of parameters are closely related to the degree distribution and the clustering coefficient but are more convenient than them in describing the process of epidemic spreading. Moreover, we find that the networks with exponential distribution have slower spreading speed than the networks with power-law degree distribution. Numerical simulations have confirmed the theoretical predictions.     

具有非马尔可夫旅途感染的流行病传播模型研究

针对旅途中的接触可以扩大流行病传播规模的问题,在集合种群网络中考虑一种时滞旅行行为和旅途中的疾病传播和恢复过程,构建具有非马尔可夫旅途感染的传播模型并利用计算机仿真模拟系统中的传播过程。基于微观马尔可夫链方法,构建预测疾病流行阈值的理论框架。仿真结果表明,旅途感染可以促进流行病在旅途中的传播,抑制其在种群内的传播;旅途时长和旅途接触概率能够改变流行病的演化趋势。这些结果有助于理解旅途感染如何影响流行病的传播。     

Epidemic spreading model of complex dynamical network with the heterogeneity of nodes

In this paper, we model epidemic spreading by considering the mobility of nodes in complex dynamical network based on mean field theory using differential equations. Moreover, a resistance factor which can characterise the impact of individual's difference on the propagation dynamics in complex dynamical network is proposed by considering the influence of total number of connections and the continuous time to remain in contact. The effect of heterogeneity on the evolution process of propagation dynamics is explored by simulation. Extensive simulations are conducted to study the key influence parameters and the influence of them on the spreading dynamics, which are helpful to the understanding of epidemic spreading mechanism and the designing of effective control strategies.