Adaptive bridge control strategy for opinion evolution on social networks

In this paper, we present an efficient opinion control strategy for complex networks, in particular, for social networks. The proposed adaptive bridge control (ABC) strategy calls for controlling a special kind of nodes named bridge and requires no knowledge of the node degrees or any other global or local knowledge, which are necessary for some other immunization strategies including targeted immunization and acquaintance immunization. We study the efficiency of the proposed ABC strategy on random networks, small-world networks, scale-free networks, and the random networks adjusted by the edge exchanging method. Our results show that the proposed ABC strategy is efficient for all of these four kinds of networks. Through an adjusting clustering coefficient by the edge exchanging method, it is found out that the efficiency of our ABC strategy is closely related with the clustering coefficient. The main contributions of this paper can be listed as follows: (1) A new high-order social network is proposed to describe opinion dynamic. (2) An algorithm, which does not require the knowledge of the nodes' degree and other global∕local network structure information, is proposed to control the "bridges" more accurately and further control the opinion dynamics of the social networks. The efficiency of our ABC strategy is illustrated by numerical examples. (3) The numerical results indicate that our ABC strategy is more efficient for networks with higher clustering coefficient.     

复杂网络的顶点着色及其在疾病免疫中的应用

在复杂网络研究中, 对于网络结构特征的分析已经引起了人们的极大关注, 而其中的网络着色问题却没有得到足够的重视. 为了理解网络结构与着色之间的关系, 本文研究了WS, BA网络以及不同宏观结构参量对于正常K色数的影响, 发现最大团数可以大致反映正常K色数的变化趋势, 而网络的平均度和匹配系数比异质性和聚类系数对于色数的影响更大. 对于一些实际网络的正常着色验证了本文的分析结果. 对复杂网络的顶点进行着色后, 根据独立集内任意两个顶点均不相邻的特点, 我们提出了基于独立集的免疫策略. 与全网随机免疫相比, 基于独立集的免疫策略可令网络更为脆弱, 从而有效抑制疾病的传播. 基于网络着色的独立集提供了一种崭新的免疫思路, 作为一个简单而适用的平台,有助于设计更为有效的免疫策略. 关键词： 复杂网络 正常着色 独立集 免疫策略     

Comparison of immunization strategies in geographical networks

The epidemic spread and immunizations in geographically embedded scale-free (SF) and Watts-Strogatz (WS) networks are numerically investigated. We make a realistic assumption that it takes time which we call the detection time, for a vertex to be identified as infected, and implement two different immunization strategies: one is based on connection neighbors (CN) of the infected vertex with the exact information of the network structure utilized and the other is based on spatial neighbors (SN) with only geographical distances taken into account. We find that the decrease of the detection time is crucial for a successful immunization in general. Simulation results show that for both SF networks and WS networks, the SN strategy always performs better than the CN strategy, especially for more heterogeneous SF networks at long detection time. The observation is verified by checking the number of the infected nodes being immunized. We found that in geographical space, the distance preferences in the network construction process and the geographically decaying infection rate are key factors that make the SN immunization strategy outperforms the CN strategy. It indicates that even in the absence of the full knowledge of network connectivity we can still stop the epidemic spread efficiently only by using geographical information as in the SN strategy, which may have potential applications for preventing the real epidemic spread.     

An Efficient Control Strategy of Epidemic Spreading on Scale-Free Networks

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.     

考虑网络流量的无标度网络病毒免疫策略研究

考虑网络交通流量对病毒传播行为的影响,基于平均场理论研究无标度网络上的病毒免疫策略,提出一种改进的熟人免疫机理.理论分析表明,在考虑网络交通流量影响的情况下,当免疫节点密度较小时,随机免疫几乎不能降低病毒的传播速率,而对网络实施目标免疫则能够有效抑制病毒的传播,并且选择度最大的节点进行免疫与选择介数最大的节点进行免疫的效果基本相同.研究还发现,对于网络全局信息未知的情况,与经典熟人免疫策略相比,所提出的免疫策略能够获得更好的免疫效果.通过数值仿真对理论分析进行了验证. 关键词： 无标度网络 病毒传播 交通流量 免疫策略     

Immunization for scale-free networks by random walker

Based on the random walk and the intentional random walk, we propose two types of immunization strategies which require only local connectivity information. On several typical scale-free networks, we demonstrate that these strategies can lead to the eradication of the epidemic by immunizing a small fraction of the nodes in the networks. Particularly, the immunization strategy based on the intentional random walk is extremely efficient for the assortatively mixed networks.     

Modelling the spread of sexually transmitted diseases on scale-free networks

In this paper a new model for the spread of sexually transmitted diseases (STDs) is presented. The dynamic behaviors of the model on a heterogenous scale-free (SF) network are considered, where the absence of a threshold on the SF network is demonstrated, and the stability of the disease-free equilibrium is obtained. Three immunization strategies, uniform immunization, proportional immunization and targeted immunization, are applied in this model. Analytical and simulated results are given to show that the proportional immunization strategy in the model is effective on SF networks.     

Immunization of susceptible-infected model on scale-free networks

In this paper, we investigate two major immunization strategies, random immunization and targeted immunization, of the susceptible-infected (SI) model on the Barabási-Albert (BA) networks. For the heterogeneous structure, the random strategy is quite ineffective if the vaccinated proportion is small, while the targeted one which prefers to vaccinate the individuals with the largest degree can sharply depress the epidemic spreading even only a tiny fraction of population are vaccinated. The analytical solution is also obtained, which can capture the trend of velocity change vs. the amount of vaccinated population.     

Graph partitioning induced phase transitions

We study the percolation properties of graph partitioning on random regular graphs with N vertices of degree k. Optimal graph partitioning is directly related to optimal attack and immunization of complex networks. We find that for any partitioning process (even if nonoptimal) that partitions the graph into essentially equal sized connected components (clusters), the system undergoes a percolation phase transition at f = fc = 1-2/k where f is the fraction of edges removed to partition the graph. For optimal partitioning, at the percolation threshold, we find S approximately N 0.4 where S is the size of the clusters and l approximately N 0.25 where l is their diameter. Also, we find that S undergoes multiple nonpercolation transitions for f相似文献   

Finding a better immunization strategy

The problem of finding the best strategy to immunize a population or a computer network with a minimal number of immunization doses is of current interest. It has been accepted that the targeted strategies on most central nodes are most efficient for model and real networks. We present a newly developed graph-partitioning strategy which requires 5% to 50% fewer immunization doses compared to the targeted strategy and achieves the same degree of immunization of the network. We explicitly demonstrate the effectiveness of our proposed strategy on several model networks and also on real networks.     

可变聚类无标度网络上的谣言免疫策略

提出一种聚类免疫策略,使用改进的经典谣言传播模型,在可变聚类无标度网络上研究其免疫效果.研究发现,聚类免疫的效果随着网络聚类系数的增加而变好.在不同聚类系数下,比较目标免疫、介数免疫、紧密度免疫和聚类免疫的免疫效果发现,无论网络的聚类特性如何,介数免疫始终是几种免疫策略中效果最好的,当网络聚类系数较大时,聚类免疫的效果超过紧密度免疫接近目标免疫,进一步增大网络的聚类系数,聚类免疫的效果超过目标免疫而接近介数免疫.     

Stability and topology of scale-free networks under attack and defense strategies

We study tolerance and topology of random scale-free networks under attack and defense strategies that depend on the degree k of the nodes. This situation occurs, for example, when the robustness of a node depends on its degree or in an intentional attack with insufficient knowledge of the network. We determine, for all strategies, the critical fraction p(c) of nodes that must be removed for disintegrating the network. We find that, for an intentional attack, little knowledge of the well-connected sites is sufficient to strongly reduce p(c). At criticality, the topology of the network depends on the removal strategy, implying that different strategies may lead to different kinds of percolation transitions.     

Immunization and epidemic dynamics in complex networks

We study the behavior of epidemic spreading in networks, and, in particular, scale free networks. We use the Susceptible-Infected-Removed (SIR) epidemiological model. We give simulation results for the dynamics of epidemic spreading. By mapping the model into a static bond-percolation model we derive analytical results for the total number of infected individuals. We study this model with various immunization strategies, including random, targeted and acquaintance immunization.Received: 3 November 2003, Published online: 14 May 2004PACS: 02.50.Cw Probability theory - 02.10.Ox Combinatorics; graph theory - 89.20.Hh World Wide Web, Internet - 64.60.-i General studies of phase transitions     

蒙特卡罗法计算无序激光器的阈值

提出了计算无序激光器阈值增益的模型,提出了已知平均自由程和散射强度分布得到分布函数及相应的随机变量的方法.对各向同性的光散射,导出了各分布函数,并在每次散射中得到相应的随机变量.运用蒙特卡罗法模拟光子在无序介质中的随机行走,得到了无序激光器的阈值.讨论了阈值增益与光子平均自由程、增益区半径、增益区深度、非增益区光吸收系数的关系,提出了降低阈值的方法.理论结果与实验数据是符合的. 关键词： 激光物理 阈值 蒙特卡罗 散射     

An improved local immunization strategy for scale-free networks with a high degree of clustering

The design of immunization strategies is an extremely important issue for disease or computer virus control and prevention. In this paper, we propose an improved local immunization strategy based on node’s clustering which was seldom considered in the existing immunization strategies. The main aim of the proposed strategy is to iteratively immunize the node which has a high connectivity and a low clustering coefficient. To validate the effectiveness of our strategy, we compare it with two typical local immunization strategies on both real and artificial networks with a high degree of clustering. Simulations on these networks demonstrate that the performance of our strategy is superior to that of two typical strategies. The proposed strategy can be regarded as a compromise between computational complexity and immune effect, which can be widely applied in scale-free networks of high clustering, such as social network, technological networks and so on. In addition, this study provides useful hints for designing optimal immunization strategy for specific network.     

Flexible electrically pumped random lasing from ZnO nanowires based on metal–insulator–semiconductor structure

Flexible electrically pumped random laser(RL) based on ZnO nanowires is demonstrated for the first time to our knowledge. The ZnO nanowires each with a length of 5 μm and an average diameter of 180 nm are synthesized on flexible substrate(ITO/PET) by a simple hydrothermal method. No obvious visible defect-related-emission band is observed in the photoluminescence(PL) spectrum, indicating that the ZnO nanowires grown on the flexible ITO/PET substrate have few defects. In order to achieve electrically pumped random lasing with a lower threshold, the metal–insulator–semiconductor(MIS) structure of Au/SiO_2/ZnO on ITO/PET substrate is fabricated by low temperature process. With sufficient forward bias, the as-fabricated flexible device exhibits random lasing, and a low threshold current of ～ 11.5 m A and high luminous intensity are obtained from the ZnO-based random laser. It is believed that this work offers a case study for developing the flexible electrically pumped random lasing from ZnO nanowires.     

基于传播免疫的复杂网络可控性研究

复杂网络控制反映了人类对复杂系统的认识深度和改造能力. 最新研究成果基于线性系统控制理论建立了复杂网络可控性的理论架构, 能够发现任意拓扑结构的线性时不变复杂网络中控制全部节点状态的最小驱动节点集, 但是该模型未考虑免疫节点或失效节点对控制信号传播的阻断.在继承该模型优点的前提下, 重新构建了基于传播免疫的复杂网络控制模型.在采用分属于随机免疫和目标免疫两种策略的 4个方法确定免疫节点的情况下,分析14个真实网络的可控性.结果表明:如果将网络中度数、 介数和紧密度指标较高的节点作为免疫节点,将极大地提高控制复杂网络的难度. 从而在一定程度上丰富了以往模型的结论.     

Best possible strategy for finding ground states

Finding the ground state of a system with a complex energy landscape is important for many physical problems including protein folding, spin glasses, chemical clusters, and neural networks. Such problems are usually solved by heuristic search methods whose efficacy is judged by empirical performance on selected examples. We present a proof that, within the large class of algorithms that simulate a random walk on the landscape, threshold accepting is the best possible strategy. In particular, it can perform better than simulated annealing and Tsallis statistics. Our proof is the first example of a provably optimal strategy in this area.     

Photon statistics of random lasers with resonant feedback

We have measured the photon statistics of random lasers with resonant feedback. With an increase of the pump intensity, the photon number distribution in a single mode changes continuously from Bose-Einstein distribution at the threshold to Poisson distribution well above the threshold. The second-order correlation coefficient drops gradually from 2 to 1. By comparing the photon statistics of a random laser with resonant feedback and that of a random laser with nonresonant feedback, we illustrate very different lasing mechanisms for the two types of random lasers.     

A Solvable Model of Interface Depinning in Random Media

We study the mean-field version of a model proposed by Leschhorn to describe the depinning transition of interfaces in random media. We show that evolution equations for the distribution of forces felt by the interface sites can be written directly for an infinite system. For a flat distribution of random local forces the value of the depinning threshold can be obtained exactly. In the case of parallel dynamics (all unstable sites move simultaneously), due to the discrete character of the interface heights allowed in the model, the motion of the center of mass is non-uniform in time in the moving phase close to the threshold, and the mean interface velocity vanishes with a square-root singularity.