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1.
Realistic networks display not only a complex topological structure, but also a heterogeneous distribution of weights in connection strengths. In addition, the information spreading through a complex network is often associated with time delays due to the finite speed of signal transmission over a distance. Hence, the weighted complex network with coupling delays have meaningful implications in real world, and resultantly gains increasing attention in various fields of science and engineering. Based on the theory of asymptotic stability of linear time-delay systems, synchronization stability of the weighted complex dynamical network with coupling delays is investigated, and simple criteria are obtained for both delay-independent and delay-dependent stabilities of synchronization states. The obtained criteria in this paper encompass the established results in the literature as special cases. Some examples are given to illustrate the theoretical results.  相似文献   

2.
We present a novel and effective method for controlling epidemic spreading on complex networks, especially on scale-free networks. The proposed strategy is performed by deleting edges according to their significances (the significance of an edge is defined as the product of the degrees of two nodes of this edge). In contrast to other methods, e.g., random immunization, proportional immunization, targeted immunization, acquaintance immunization and so on, which mainly focus on how to delete nodes to realize the control of epidemic spreading on complex networks, our method is more effective in realizing the control of epidemic spreading on complex networks, moreover, such a method can better retain the integrity of complex networks.  相似文献   

3.
韦笃取  张波  丘东元  罗晓曙 《中国物理 B》2010,19(10):100513-100513
Recent experimental evidence suggests that some brain activities can be assigned to small-world networks. In this work, we investigate how the topological probability p and connection strength C affect the activities of discrete neural networks with small-world (SW) connections. Network elements are described by two-dimensional map neurons (2DMNs) with the values of parameters at which no activity occurs. It is found that when the value of p is smaller or larger, there are no active neurons in the network, no matter what the value of connection strength is; for a given appropriate connection strength, there is an intermediate range of topological probability where the activity of 2DMN network is induced and enhanced. On the other hand, for a given intermediate topological probability level, there exists an optimal value of connection strength such that the frequency of activity reaches its maximum. The possible mechanism behind the action of topological probability and connection strength is addressed based on the bifurcation method. Furthermore, the effects of noise and transmission delay on the activity of neural network are also studied.  相似文献   

4.
In this paper, a dynamic epidemic control model on the uncorrelated complex networks is proposed. By means of theoretical analysis, we found that the new model has a similar epidemic threshold as that of the susceptible-infectedrecovered (SIR) model on the above networks, but it can reduce the prevalence of the infected individuals remarkably. This result may help us understand epidemic spreading phenomena on real networks and design appropriate strategies to control infections.  相似文献   

5.
We introduce the generalized rumor spreading model and investigate some properties of this model on different complex social networks. Despite pervious rumor models that both the spreader-spreader (SS) and the spreader-stifler (SR) interactions have the same rate α, we define α(1) and α(2) for SS and SR interactions, respectively. The effect of variation of α(1) and α(2) on the final density of stiflers is investigated. Furthermore, the influence of the topological structure of the network in rumor spreading is studied by analyzing the behavior of several global parameters such as reliability and efficiency. Our results show that while networks with homogeneous connectivity patterns reach a higher reliability, scale-free topologies need a less time to reach a steady state with respect the rumor.  相似文献   

6.
In this paper,a new susceptible-infected-susceptible (SIS) model on complex networks with imperfect vaccination is proposed.Two types of epidemic spreading patterns (the recovered individuals have or have not immunity) on scale-free networks are discussed.Both theoretical and numerical analyses are presented.The epidemic thresholds related to the vaccination rate,the vaccination-invalid rate and the vaccination success rate on scale-free networks are demonstrated,showing different results from the reported observations.This reveals that whether or not the epidemic can spread over a network under vaccination control is determined not only by the network structure but also by the medicine’s effective duration.Moreover,for a given infective rate,the proportion of individuals to vaccinate can be calculated theoretically for the case that the recovered nodes have immunity.Finally,simulated results are presented to show how to control the disease prevalence.  相似文献   

7.
赵力  杨捷  谢群英  田苗 《中国物理 B》2012,21(9):90304-090304
Using the -mapping topological theory, we study the topological structure of vortex lines in a two-dimensional generalized Gross-Pitaevskii theory in (3+1)-dimensional space-time. We obtain the reduced dynamic equation in the framework of the two-dimensional Gross-Pitaevskii theory, from which a conserved dynamic quantity is derived on the stable vortex lines. Such equations can also be used to discuss Bose-Einstein condensates in heterogeneous and highly nonlinear systems. We obtain an exact dynamic equation with a topological term, which is ignored in traditional hydrodynamic equations. The explicit expression of vorticity as a function of the order parameter is derived, where the δ function indicates that the vortices can only be generated from the zero points of Φ and are quantized in terms of the Hopf indices and Brouwer degrees. The -mapping topological current theory also provides a reasonable way to study the bifurcation theory of vortex lines in the two-dimensional Gross-Pitaevskii theory.  相似文献   

8.
Le He  Linhe Zhu 《理论物理通讯》2021,73(3):35002-22
The coronavirus disease 2019(COVID-19)has been widely spread around the world,and the control and behavior dynamics are still one of the important research directions in the world.Based on the characteristics of COVID-19’s spread,a coupled disease-awareness model on multiplex networks is proposed in this paper to study and simulate the interaction between the spreading behavior of COVID-19 and related information.In the layer of epidemic spreading,the nodes can be divided into five categories,where the topology of the network represents the physical contact relationship of the population.The topological structure of the upper network shows the information interaction among the nodes,which can be divided into aware and unaware states.Awareness will make people play a positive role in preventing the epidemic diffusion,influencing the spread of the disease.Based on the above model,we have established the state transition equation,through the microscopic Markov chain approach(MMCA),and proposed the propagation threshold calculation method under the epidemic model.Furthermore,MMCA iteration and the Monte Carlo method are simulated on the static network and dynamic network,respectively.The current results will be beneficial to the study of COVID-19,and propose a more rational and effective model for future research on epidemics.  相似文献   

9.
We propose a new approach to rigorously prove the existence of the steady-state degree distribution for the BA network. The approach is based on a vector Markov chain of vertex numbers in the network evolving process. This framework provides a rigorous theoretical basis for the rate equation approach which has been widely applied to many problems in the field of complex networks, e.g., epidemic spreading and dynamic synchronization.  相似文献   

10.
The duplication and divergence process is ubiquitous in nature and man-made networks. Motivated by the duplication-divergence mechanism which depicts the growth of protein networks, we propose a weighted network model in which topological evolution is coupled with weight dynamics. Large scale numerical results indicate that our model can naturally generate networks with power-law-like distributions of degree, strength and weight. The degree-strength correlation is illustrated as well. These properties are in agreement well with empirical data observed in real-world systems. Furthermore, by altering the retention probability δ, weighted, structured exponential networks are realized.  相似文献   

11.
The congestion transition triggered by multiple walkers walking along the shortest path on complex networks is numerically investigated. These networks are composed of nodes that have a finite capacity in analogy to the buffer memory of a computer. It is found that a transition from free-flow phase to congestion phase occurs at a critical walker density fc, which varies for complex networks with different topological structures. The dynamic pictures of congestion for networks with different topological structures show that congestion on scale-free networks is a percolation process of congestion clusters, while the dynamics of congestion transition on non-scale-free networks is mainly a process of nucleation.  相似文献   

12.
基于移动社交网络的谣言传播动力学研究   总被引:3,自引:0,他引:3       下载免费PDF全文
王辉  韩江洪  邓林  程克勤 《物理学报》2013,62(11):110505-110505
本文在CSR传播模型的基础上提出基于移动社交网络的CSR的谣言传播模型. 改进了CSR模型的传播规则和传播动力学方程, 使得更符合移动SNS上用户的使用习惯. 在CSR模型中的接受概率数学模型基础上, 考虑个人接受阈值对接受概率的影响, 更符合人类接受谣言的心理学特点. 本文对该传播模型进行了理论分析. 并在仿真实验中, 利用多agent仿真平台对新模型和CSR模型以及SIR模型 在匀质网络和异质网络中的传播效果进行了对比研究, 从实验的结果来看, 新的谣言传播模型在匀质网络中传播范围更广, 传播速度更快. 新模型具有初值敏感性的特点. 关键词: 复杂网络 移动社交网络 谣言传播  相似文献   

13.
It is commonly accepted that realistic networks can display not only a complex topological structure, but also a heterogeneous distribution of connection weights. In addition, time delay is inevitable because the information spreading through a complex network is characterized by the finite speeds of signal transmission over a distance. Weighted complex networks with coupling delays have been gaining increasing attention in various fields of science and engineering. Some of the topics of most concern in the field of weighted complex networks are finding how the synchronizability depends on various parameters of the network including the coupling strength, weight distribution and delay. On the basis of the theory of asymptotic stability of linear time-delay systems with complex coefficients, the synchronization stability of weighted complex dynamical networks with coupling delays is investigated, and simple criteria are obtained for both delay-independent and delay-dependent stabilities of the synchronization state. Finally, an example is given as an illustration testing the theoretical results.  相似文献   

14.
Alen Lan?i? 《Physica A》2011,390(1):65-76
Disease spreading on complex networks is studied in SIR model. Simulations on empirical complex networks reveal two specific regimes of disease spreading: local containment and epidemic outbreak. The variables measuring the extent of disease spreading are in general characterized by a bimodal probability distribution. Phase diagrams of disease spreading for empirical complex networks are introduced. A theoretical model of disease spreading on m-ary tree is investigated both analytically and in simulations. It is shown that the model reproduces qualitative features of phase diagrams of disease spreading observed in empirical complex networks. The role of tree-like structure of complex networks in disease spreading is discussed.  相似文献   

15.
Epidemic spreading in scale-free networks   总被引:63,自引:0,他引:63  
The Internet has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of computer viruses. We analyze real data from computer virus infections and find the average lifetime and persistence of viral strains on the Internet. We define a dynamical model for the spreading of infections on scale-free networks, finding the absence of an epidemic threshold and its associated critical behavior. This new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.  相似文献   

16.
王金龙  刘方爱  朱振方 《物理学报》2015,64(5):50501-050501
根据在线社交网络信息传播特点和目前社交网络传播模型研究中存在的问题, 本文定义了网络用户之间的相互影响力函数, 在此基础上提出了一种基于用户相对权重的社交网络信息传播模型, 并对网络中的传播路径及传播过程进行了分析, 讨论了不同路径的信息传播影响力.为验证模型的有效性, 将传统的SIR模型和本文模型在六类不同网络拓扑下进行了仿真实验.仿真结果表明, 两类模型在均匀网络中没有明显差异, 但在非均匀网络中本文模型更能体现真实网络特点, 实验同时验证了节点的地位影响着信息的传播, 并且发现英文社交平台Twitter和中文社交平台新浪微博在拓扑结构上具备一定相似性.  相似文献   

17.
Since cognitive radio (CR) networks could solve the spectrum scarcity problem, they have drawn much research in recent years. Artificial intelligence(AI) is introduced into CRs to learn from and adapt to their environment. Nonetheless, AI brings in a new kind of attacks specific to CR networks. The most powerful one is a self-propagating AI virus. And no spreading properties specific to this virus have been reported in the literature. To fill this research gap, we propose a virus spreading model of an AI virus by considering the characteristics of CR networks and the behavior of CR users. Several important observations are made from the simulation results based on the model. Firstly, the time taken to infect the whole network increases exponentially with the network size. Based on this result, CR network designers could calculate the optimal network size to slow down AI virus propagation rate. Secondly, the anti-virus performance of static networks to an AI virus is better than dynamic networks. Thirdly, if the CR devices with the highest degree are initially infected, the AI virus propagation rate will be increased substantially. Finally, it is also found that in the area with abundant spectrum resource, the AI virus propagation speed increases notably but the variability of the spectrum does not affect the propagation speed much.  相似文献   

18.
We generate a directed weighted complex network by a method based on Markov transition probability to represent an experimental two-phase flow. We first systematically carry out gas-liquid two-phase flow experiments for measuring the time series of flow signals. Then we construct directed weighted complex networks from various time series in terms of a network generation method based on Markov transition probability. We find that the generated network inherits the main features of the time series in the network structure. In particular, the networks from time series with different dynamics exhibit distinct topological properties. Finally, we construct two-phase flow directed weighted networks from experimental signals and associate the dynamic behavior of gas-liquid two-phase flow with the topological statistics of the generated networks. The results suggest that the topological statistics of two-phase flow networks allow quantitative characterization of the dynamic flow behavior in the transitions among different gas-liquid flow patterns.  相似文献   

19.
苏晓萍  宋玉蓉 《物理学报》2015,64(2):20101-020101
识别复杂网络中的关键节点对网络结构优化和鲁棒性增强具有十分重要的意义. 经典的关键节点测量方法在一定程度上能够辨识网络中影响力节点, 但存在一定局限性: 局部中心性测量方法仅考虑节点邻居的数目, 忽略了邻居间的拓扑关系, 不能在计算中反映邻居节点间的相互作用; 全局测量方法则由于算法本身的复杂性而不能应用于大规模社会网络的分析, 另外, 经典的关键节点测量方法也没有考虑社会网络特有的社区特征. 为高效、准确地辨识具有社区结构的社会网络中最具影响力节点, 提出了一种基于节点及其邻域结构洞的局部中心性测量方法, 该方法综合考虑了节点的邻居数量及其与邻居间的拓扑结构, 在节点约束系数的计算中同时体现了节点的度属性和“桥接”属性. 利用SIR(易感-感染-免疫)模型在真实社会网络数据上对节点传播能力进行评价后发现, 所提方法可以准确地评价节点的传播能力且具有强的鲁棒性.  相似文献   

20.
Defining the importance of nodes in a complex network has been a fundamental problem in analyzing the structural organization of a network, as well as the dynamical processes on it. Traditionally, the measures of node importance usually depend either on the local neighborhood or global properties of a network. Many real-world networks, however, demonstrate finely detailed structure at various organization levels, such as hierarchy and modularity. In this paper, we propose a multiscale node-importance measure that can characterize the importance of the nodes at varying topological scale. This is achieved by introducing a kernel function whose bandwidth dictates the ranges of interaction, and meanwhile, by taking into account the interactions from all the paths a node is involved. We demonstrate that the scale here is closely related to the physical parameters of the dynamical processes on networks, and that our node-importance measure can characterize more precisely the node influence under different physical parameters of the dynamical process. We use epidemic spreading on networks as an example to show that our multiscale node-importance measure is more effective than other measures.  相似文献   

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