Optimal Capacity Allocation on Heterogeneous Complex Transport Networks

We study how the node delivering capacity is allocated so that the traffic transport efficiency can be enhanced maximally. Network heterogeneity of degree distribution and processing delay of the traffic are considered. An explicit analytical solution is provided, which is based on the M/M/1 queueing theory and optimization principle, provided that the network structure and routing strategy are given. In particular, we extend the relevant conclusions in the literature [Europhys. Lett. 83 （2008） 28001]. Finally, an order parameter simulation example by comparing results with those obtained via simple capacity allocation in large Barabasi—Albert （BA） scale-free network is provided to illustrate the effectiveness of the theoretical results.     

Outer Synchronization of Complex Networks by Impulse

This paper investigates outer synchronization of complex networks, especially, outer complete synchronization and outer anti-synchronization between the driving network and the response network. Employing the impulsive control method which is uncontinuous, simple, efficient, low-cost and easy to implement in practical applications, we obtain some sufficient conditions of outer complete synchronization and outer anti-synchronization between two complex networks. Numerical simulations demonstrate the effectiveness of the proposed impulsive control scheme.     

RWA方法对WDM网络P圈优化性能的影响

研究不同的路由和波长分配（RWA）方法对无波长变换WDM网络P圈优化性能的影响.提出了用负载均衡的方法对各波长层的工作容量进行均衡,以降低网络总容量.分别研究了动态分层通用RWA（DL-GRWA）、最短路径RWA（SP-RWA）、动态分层负载均衡（DL-LB）、最短路径负载均衡（SP-LB）、固定波长负载均衡（FW-LB）5种方法对网络总容量的影响.仿真发现,无论何种RWA方法,随着圈最大跳数限制的变大,网络总容量都逐渐降低,其中SP-LB方法所需要的网络总容量最小.     

Amplified Signal Response by Neuronal Diversity on Complex Networks

The effect of diversity on dynamics of coupled FitzHugh-Nagumo neurons on complex networks is numerically investigated, where each neuron is subjected to an external subthreshold signal. With the diversity the network is a mixture of excitable and oscillatory neurons, and the diversity is determined by the variance of the system＇s parameter. The complex network is constructed by randomly adding long-range connections （shortcuts） on a nearest-neighbouring coupled one-dimensional chain. Numerical results show that external signals are maximally magnified at an intermediate value of the diversity, as in the case of well-known stochastic resonance, burthermore, the effects of the number of shortcuts and coupled strength on the diversity-induced phenomena are also discussed. These findings exhibit that the diversity may play a constructive role in response to external signal, and highlight the importance of the diversity on such complex networks.     

Geographical Effects on Complex Networks

We investigate how the geographical structure of a complex network affects its network topology, synchronization and the average spatial length of edges. The geographical structure means that the connecting probability of two nodes is related to the spatial distance of the two nodes. Our simulation results show that the geographical structure changes the network topology. The synchronization tendency is enhanced and the average spatial length of edges is enlarged when the node can randomly connect to the further one. Analytic results support our understanding of the phenomena.     

Geographical Effects on Complex Networks

We investigate how the geographical structure of a complex network affects its network topology, synchronization and the average spatial length of edges. The geographical structure means that the connecting probability of two nodes is related to the spatial distance of the two nodes. Our simulation results show that the geographical structure changes the network topology. The synchronization tendency is enhanced and the average spatial length of edges is enlarged when the node can randomly connect to the further one. Analytic results support our understanding of the phenomena.     

Epidemic Diffusion on Complex Networks

Both diffusion and epidemic are well studied in the stochastic systems and complex networks, respectively. Here we combine these two fields and study epidemic diffusion in complex networks. Instead of studying the threshold of infection, which was focused on in previous works, we focus on the diffusion behayiour. We find that the epidemic diffusion in a complex network is an anomalous superdiffusion with varying diffusion exponent and that γ is influenced seriously by the network structure, such as the clustering coefficient and the degree distribution. Numerical simulations have confirmed the theoretical predictions.     

物理真随机码发生器随机性分析

对物理随机码发生器的物理参量与其产生的随机码序列的随机性关系进行了分析.根据量子保密通信对随机码序列的随机性的要求,分析了常见的随机码发生器产生的随机码的随机性,给出了利用随机高斯噪音经比较器产生随机码的随机码发生器的随机性公式.     

Properties of Bottleneck on Complex Networks

The traffic bottleneck plays a key role in most of the natural and artificial network. Here we present a simply model for bottleneck dynamical characteristics consideration the reliability on the complex network by taking into account the network topology characteristics and system size. We find that there is a critical rate of flow generation below which the network traffic is free but above which traffic congestion occurs. Also, it is found that random networks have larger critical flow generating rate than scale free ones. Analytical results may be practically useful for designing networks, especially for the urban traffic network.     

Biased Random Number Generator Based on Bell's Theorem

We propose a biased random number generation protocol whose randomness is based on the violation of the Clauser-Horne inequality.Non-maximally entangled state is used to maximize the Bell violation.Due to the rotational asymmetry of the quantum state,the ratio of Os to Is varies with the measurement bases.The experimental partners can then use their measurement outcomes to generate the biased random bit string.The bias of their bit string can be adjusted by altering their choices of measurement bases.When this protocol is implemented in a device-independent way,we show that the bias of the bit string can stiii be ensured under the collective attack.     

Optimal Cooperation Enhancement on Spatial Networks

We investigate the evolutionary Prisoner＇s dilemma game on the simplest spatial networks constructed as geometrical graphs. The optimal cooperation enhancement against the topology randomness is found. It is proposed that the optimal behavior of the cooperation results from the competition between individuals with high degrees and with low degrees： the former assists the formation of cooperator clusters and the latter tends to prevent the formation of such dusters.     

Different Epidemic Models on Complex Networks

Models for diseases spreading are not just limited to SIS or SIR. For instance, for the spreading of AIDS/HIV, the susceptible individuals can be classified into different cases according to their immunity, and similarly, the infected individuals can be sorted into different classes according to their infectivity. Moreover, some diseases may develop through several stages. Many authors have shown that the individuals＇ relation can be viewed as a complex network. So in this paper, in order to better explain the dynamical behavior of epidemics, we consider different epidemic models on complex networks, and obtain the epidemic threshold for each ease. Finally, we present numerical simulations for each case to verify our results.     

Non-consensus Opinion Models on Complex Networks

Social dynamic opinion models have been widely studied to understand how interactions among individuals cause opinions to evolve. Most opinion models that utilize spin interaction models usually produce a consensus steady state in which only one opinion exists. Because in reality different opinions usually coexist, we focus on non-consensus opinion models in which above a certain threshold two opinions coexist in a stable relationship. We revisit and extend the non-consensus opinion (NCO) model introduced by Shao et al. (Phys. Rev. Lett. 103:01870, 2009). The NCO model in random networks displays a second order phase transition that belongs to regular mean field percolation and is characterized by the appearance (above a certain threshold) of a large spanning cluster of the minority opinion. We generalize the NCO model by adding a weight factor W to each individual’s original opinion when determining their future opinion (NCOW model). We find that as W increases the minority opinion holders tend to form stable clusters with a smaller initial minority fraction than in the NCO model. We also revisit another non-consensus opinion model based on the NCO model, the inflexible contrarian opinion (ICO) model (Li et al. in Phys. Rev. E 84:066101, 2011), which introduces inflexible contrarians to model the competition between two opinions in a steady state. Inflexible contrarians are individuals that never change their original opinion but may influence the opinions of others. To place the inflexible contrarians in the ICO model we use two different strategies, random placement and one in which high-degree nodes are targeted. The inflexible contrarians effectively decrease the size of the largest rival-opinion cluster in both strategies, but the effect is more pronounced under the targeted method. All of the above models have previously been explored in terms of a single network, but human communities are usually interconnected, not isolated. Because opinions propagate not only within single networks but also between networks, and because the rules of opinion formation within a network may differ from those between networks, we study here the opinion dynamics in coupled networks. Each network represents a social group or community and the interdependent links joining individuals from different networks may be social ties that are unusually strong, e.g., married couples. We apply the non-consensus opinion (NCO) rule on each individual network and the global majority rule on interdependent pairs such that two interdependent agents with different opinions will, due to the influence of mass media, follow the majority opinion of the entire population. The opinion interactions within each network and the interdependent links across networks interlace periodically until a steady state is reached. We find that the interdependent links effectively force the system from a second order phase transition, which is characteristic of the NCO model on a single network, to a hybrid phase transition, i.e., a mix of second-order and abrupt jump-like transitions that ultimately becomes, as we increase the percentage of interdependent agents, a pure abrupt transition. We conclude that for the NCO model on coupled networks, interactions through interdependent links could push the non-consensus opinion model to a consensus opinion model, which mimics the reality that increased mass communication causes people to hold opinions that are increasingly similar. We also find that the effect of interdependent links is more pronounced in interdependent scale free networks than in interdependent Erd?s Rényi networks.     

Different Epidemic Models on Complex Networks

Models for diseases spreading are not just limited to SIS or SIR. For instance, for the spreading of AIDS/HIV, the susceptible individuals can be classified into different cases according to their immunity, and similarly, the infected individuals can besorted into different classes according to their infectivity. Moreover, some diseases may develop through several stages. Many authors have shown that the individuals' relation can be viewed as a complex network. So in this paper, in order to better explain the dynamical behavior of epidemics, we consider different epidemicmodels on complex networks, and obtain the epidemic threshold for each case. Finally, we present numerical simulations for each case to verify our results.     

Multiple Partial Attacks on Complex Networks

We numerically investigate the effect of four kinds of partial attacks of multiple targets on the Barabási Albert （BA） scale-free network and the Erdos-Rényi （ER） random network. Comparing with the effect of single target complete knockout we find that partial attacks of multiple targets may produce an effect higher than the complete knockout of a single target on both BA scale-free network and ER random network. We also find that the BA scale-free network seems to be more susceptible to multi-target partial attacks than the ER random network.     

Synchronizing Complex Networks by an Adaptive Adjustment Mechanism

We propose an adaptive adjustment mechanism for synchronizing complex networks, in particular for sociological or/and biological systems. We do not take it for granted that a dynamical system is put on isolated nodes and they are coupled with each other by one （or more） variable（s）, as employed in most previous models. As a replacement, we suppose that each node may have any finite number of possible states, and their evolutions with time are determined by their nearest-neighbouring （or even second-nearest-neighbouring, etc） nodes in an adaptive adjustment mechanism. It is found that synchronization can be achieved for almost all connected networks and that the scale-free property can evidently improve the synchronizing speed.     

Self-Organized Optimization of Transport on Complex Networks

We propose a self-organized optimization mechanism to improve the transport capacity of complex gradient networks.We find that,regardless of network topology,the congestion pressure can be strongly reduced by the self-organized optimization mechanism.Furthermore,the random scale-free topology is more efficient to reduce congestion compared with the random Poisson topology under the optimization mechanism.The reason is that the optimization mechanism introduces the correlations between the gradient field and the local topology of the substrate network.Due to the correlations,the cutoff degree of the gradient network is strongly reduced and the number of the nodes exerting their maximal transport capacity consumedly increases.Our work presents evidence supporting the idea that scale-free networks can efficiently improve their transport capacity by selforganized mechanism under gradient-driven transport mode.