Quasi Scale-Free Parameter Networks of Ion Source Experiments

We study some parameter networks of ion source adjustment experiments and find quasi-scale-free characters.Their nodes are parameter settings of every discharge and connected by each of adjacent discharge. Their cumulative degree distributions obey the expression of stretched exponential distribution‘s rank-ordering form,and their degree distributions exhibit product-form by two functions, one is similar to an exponential form, and the other is close to a power law. An index is presented to measure how this distribution is close to the power law and how this distribution is used in the analysis of these parameter networks. The mode of parameter adjustment decides that the quasi-scale-free networks are formed naturally.     

Prisoner's Dilemma Game on Two Types of Positively Correlated Networks

We study the effects of degree correlations on the evolution of cooperation in the prisoner＇s dilemma game with individuals located on two types of positively correlated networks. It is shown that the positive degree correlation can either promote or inhibit the emergence of cooperation depending on network configurations. Furthermore, we investigate the probability to cooperate as a function of connectivity degree, and find that high-degree individuals generally have a higher tendency to cooperate. Finally, it is found that small-degree individuals usually change their strategy more frequently, and such change is shown to be unfavourable to cooperation for both kinds of networks.     

Evolutionary Prisoners' Dilemma Game on Scale-Free Networks with Degree-Degree Correlation

Degree-degree correlation and heterogeneity in degree are important topological properties characterizing scale-free networks. We consider an evolutionary prisoners＇ dilemma game on scale-free networks and investigate how degree-degree correlation influences cooperation. It is found that the cooperator frequency displays resonance-like behavior with the variation of Pearson correlation coefficient. A measure on local heterogeneity in a network is proposed and it is realized that cooperation is proportional to the local heterogeneity.     

Catastrophes in Scale-Free Networks

An alternative model about cascading occurrences caused by perturbation is established to search the mechanism because catastrophes in networks occur. We investigate the avalanche dynamics of our model on two-dimensional Euclidean lattices and scale-free networks and find that the avalanche dynamic behaviour is sensitive to the topological structure of networks. The simulation results show that the catastrophes occur much more frequently in scale-free networks than those in Euclidean lattices, and the greatest catastrophe in scale-free networks is much more serious than that in Euclidean lattices. Furthermore, we have studied how to reduce the catastrophes‘degree, and have schemed out an effective strategy, called the targeted safeguard strategy for scale-free networks.     

Pair correlations in scale-free networks

Correlation between nodes is found to be a common and important property in many complex networks. Here we investigate degree correlations of the Barabasi－Albert (BA) scale-free model with both analytical results and simulations, and find two neighbouring regions, a disassortative one for low degrees and a neutral one for high degrees. The average degree of the neighbours of a randomly picked node is expected to diverge in the limit of infinite network size. As a generalization of the concept of correlation, we also study the correlations of other scalar properties, including age and clustering coefficient. Finally we propose a correlation measurement in bipartite networks.     

Network correlation between investor’s herding behavior and overconfidence behavior

It is generally accepted that herding behavior and overconfidence behavior are unrelated or even mutually exclusive.However,these behaviors can both lead to some similar market anomalies,such as excessive trading volume and volatility in the stock market.Due to the limitation of traditional time series analysis,we try to study whether there exists network relevance between the investor’s herding behavior and overconfidence behavior based on the complex network method.Since the investor’s herding behavior is based on market trends and overconfidence behavior is based on past performance,we convert the time series data of market trends into a market network and the time series data of the investor’s past judgments into an investor network.Then,we update these networks as new information arrives at the market and show the weighted in-degrees of the nodes in the market network and the investor network can represent the herding degree and the confidence degree of the investor,respectively.Using stock transaction data of Microsoft,US S&P 500 stock index,and China Hushen 300 stock index,we update the two networks and find that there exists a high similarity of network topological properties and a significant correlation of node parameter sequences between the market network and the investor network.Finally,we theoretically derive and conclude that the investor’s herding degree and confidence degree are highly related to each other when there is a clear market trend.     

Prisoner＇s Dilemma Game with Nonlinear Attractive Effect on Regular Small-World Networks

We investigate a spatial Prisoner＇s Dilemma game with nonlinear attractive effect on regular small-world networks. The players located on the sites of networks can either cooperate with their neighbours or defect. In every generation, each player updates its strategy by firstly choosing one of the neighbours with a probability proportional to .A^α denoting the attractiveness of the neighbour, where .4 is the collected payoff and ^α （-〉0） is a free parameter characterizing the extent of nonlinear effect. Then each player adopts its strategy with a probability dependent on their payoff difference. Using Monte Carlo simulations, we investigate the density pc of cooperators in the stationary state for various values of α and the rewiring probability q of the network. It is shown that the introduction of such attractive effect remarkably promotes the emergence and persistence of cooperation over a wide range of the temptation to defect for the same network structures. We also point out that long-range connections either enhance or inhibit the cooperation, which depends on the value of α and the payoff parameter b.     

Synchronization of Phase Oscillators in Networks with Certain Frequency Sequence

Synchronization of Kuramoto phase oscillators arranged in real complex neural networks is investigated. It is shown that the synchronization greatly depends on the sets of natural frequencies of the involved oscillators. The influence of network connectivity heterogeneity on synchronization depends particularly on the correlation between natural frequencies and node degrees. This finding implies a potential application that inhibiting the effects caused by the changes of network structure can be bManced out nicely by choosing the correlation parameter appropriately.     

The effects of degree correlations on network topologies and robustness

Complex networks have been applied to model numerous interactive nonlinear systems in the real world. Knowledge about network topology is crucial to an understanding of the function, performance and evolution of complex systems. In the last few years, many network metrics and models have been proposed to investigate the network topology, dynamics and evolution. Since these network metrics and models are derived from a wide range of studies, a systematic study is required to investigate the correlations among them. The present paper explores the effect of degree correlation on the other network metrics through studying an ensemble of graphs where the degree sequence (set of degrees) is fixed. We show that to some extent, the characteristic path length, clustering coefficient, modular extent and robustness of networks are directly influenced by the degree correlation.     

Self-similarity, small-world, scale-free scaling, disassortativity, and robustness in hierarchical lattices

In this paper, firstly, we study analytically the topological features of a family of hierarchical lattices (HLs) from the view point of complex networks. We derive some basic properties of HLs controlled by a parameter q: scale-free degree distribution with exponent γ=2+ln 2/(ln q), null clustering coefficient, power-law behavior of grid coefficient, exponential growth of average path length (non-small-world), fractal scaling with dimension d_B=ln (2q)/(ln 2), and disassortativity. Our results show that scale-free networks are not always small-world, and support the conjecture that self-similar scale-free networks are not assortative. Secondly, we define a deterministic family of graphs called small-world hierarchical lattices (SWHLs). Our construction preserves the structure of hierarchical lattices, including its degree distribution, fractal architecture, clustering coefficient, while the small-world phenomenon arises. Finally, the dynamical processes of intentional attacks and collective synchronization are studied and the comparisons between HLs and Barabási-Albert (BA) networks as well as SWHLs are shown. We find that the self-similar property of HLs and SWHLs significantly increases the robustness of such networks against targeted damage on hubs, as compared to the very vulnerable non fractal BA networks, and that HLs have poorer synchronizability than their counterparts SWHLs and BA networks. We show that degree distribution of scale-free networks does not suffice to characterize their synchronizability, and that networks with smaller average path length are not always easier to synchronize.     

Tsallis Mapping in Growing Complex Networks with Fitness

We introduce Tsallis mapping in Bianconi-Barab'asi (B-B) fitness model of growing networks.This mapping addresses the dynamical behavior of the fitness model within the framework of nonextensive statistics mechanics,which is characterized by a dimensionless nonextensivity parameter q.It is found that this new phenomenological parameter plays an important role in the evolution of networks:the underlying evolving networks may undergo a different phases depending on the q exponents,comparing to the original B-B fitness model,and the corresponding critical transition temperature T C is also identified.     

Rank-based model for weighted network with hierarchical organization and disassortative mixing

In this paper, we study a rank-based model for weighted network. The evolution rule of the network is based on the ranking of node strength, which couples the topological growth and the weight dynamics. Analytically and by simulations, we demonstrate that the generated networks recover the scale-free distributions of degree and strength in the whole region of the growth dynamics parameter (α>0). Moreover, this network evolution mechanism can also produce scale-free property of weight, which adds deeper comprehension of the networks growth in the presence of incomplete information. We also characterize the clustering and correlation properties of this class of networks. It is showed that at α=1 a structural phase transition occurs, and for α>1 the generated network simultaneously exhibits hierarchical organization and disassortative degree correlation, which is consistent with a wide range of biological networks.     

Linear analysis of degree correlations in complex networks

Many real-world networks such as the protein–protein interaction networks and metabolic networks often display nontrivial correlations between degrees of vertices connected by edges. Here, we analyse the statistical methods used usually to describe the degree correlation in the networks, and analytically give linear relation in the degree correlation. It provides a simple and interesting perspective on the analysis of the degree correlation in networks, which is usefully complementary to the existing methods for degree correlation in networks. Especially, the slope in the linear relation corresponds exactly to the degree correlation coefficient in networks, meaning that it can not only characterize the level of degree correlation in networks, but also reflects the speed that the average nearest neighbours’ degree varies with the vertex degree. Finally, we applied our results to several real-world networks, validating the conclusions of the linear analysis of degree correlation. We hope that the work in this paper can be helpful for further understanding the degree correlation in complex networks.     

General dynamics of topology and traffic on weighted technological networks

For most technical networks, the interplay of dynamics, traffic, and topology is assumed crucial to their evolution. In this Letter, we propose a traffic-driven evolution model of weighted technological networks. By introducing a general strength-coupling mechanism under which the traffic and topology mutually interact, the model gives power-law distributions of degree, weight, and strength, as confirmed in many real networks. Particularly, depending on a parameter W that controls the total weight growth of the system, the nontrivial clustering coefficient C, degree assortativity coefficient r, and degree-strength correlation are all consistent with empirical evidence.     

A Mixed Mechanism of Weighted-Driven and Inner Selection in Networks

For most networks, the weight of connection is changing with their attachment and inner affinity. By introducing a mixed mechanism of weighted-driven and inner selection, the model exhibits wide range power-law distributions of node strength and edge weight, and the exponent can be adjusted by not only the parameter δ but also the probability q. Furthermore, we investigate the weighted average shortest distance, clustering coefficient, and the correlation of our network. In addition, the weighted assortativity coefficient which characterizes important information of weighted topological networks has been discussed, but the variation of coefficients is much smaller than the former researches.     

Balls-in-boxes condensation on networks

We discuss two different regimes of condensate formation in zero-range processes on networks: on a q-regular network, where the condensate is formed as a result of a spontaneous symmetry breaking, and on an irregular network, where the symmetry of the partition function is explicitly broken. In the latter case we consider a minimal irregularity of the q-regular network introduced by a single Q node with degree Q>q. The statics and dynamics of the condensation depend on the parameter alpha=ln Q/q, which controls the exponential falloff of the distribution of particles on regular nodes and the typical time scale for melting of the condensate on the Q node, which increases exponentially with the system size N. This behavior is different than that on a q-regular network, where alpha=0 and where the condensation results from the spontaneous symmetry breaking of the partition function, which is invariant under a permutation of particle occupation numbers on the q nodes of the network. In this case the typical time scale for condensate melting is known to increase typically as a power of the system size.     

有倾向性重连产生的反匹配网络

在具有网络结构的系统中度关联属性对于动力学行为具有重要的影响, 所以产生适当度关联网络的方法对于大量网络系统的研究具有重要的作用. 尽管产生正匹配网络的方法已经得到很好的验证, 但是产生反匹配网络的方法还没有被系统的讨论过. 重新连接网络中的边是产生度关联网络的一个常用方法. 这里我们研究使用重连方法产生反匹配无标度网络的有效性. 我们的研究表明, 有倾向的重连可以增强网络的反匹配属性. 但是有倾向重连不能使皮尔森度相关系数下降到-1, 而是存在一个依赖于网络参数的最小值. 我们研究了网络的主要参数对于网络度相关系数的影响, 包括网络尺寸, 网络的连接密度和网络节点的度差异程度. 研究表明在网络尺寸大的情况下和节点度差异性强的情况下, 重连的效果较差. 我们研究了真实Internet网络, 发现模型产生的网络经过重连不能达到真实网络的度关联系数.     

度关联无标度网络上的有倾向随机行走

有倾向随机行走是研究网络上数据包路由策略的有效方法. 由于许多真实技术网络包括互联网都具有负的度关联特征, 因此本文研究这种网络上的有倾向随机行走性质. 研究表明: 在负关联网络上粒子可以在连接度较大的节点上均匀分布, 而连接度小的节点上粒子较少; 负关联网络上随机行走的速度比非关联网络更快; 找到了负关联网络上的最佳倾向性系数, 在此情况下负关联网络上随机行走的速度远快于非关联网络. 负关联网络既可以利用度小的节点容纳粒子, 又可以利用度大的节点快速传输, 这是负关联网络上高行走效率产生的机制.