Unified Model for Generation Complex Networks with Utility Preferential Attachment

In this paper, based on the utility preferential attachment, we propose a new unified model to generate different network topologies such as scale-free, small-world and random networks. Moreover, a new network structure named super scale network is found, which has monopoly characteristic in our simulation experiments. Finally, the characteristics ofthis new network are given.     

一种基于最大流的网络结构熵

熵是可用来反映网络结构异质性的指标.针对传统熵指标不能很好反映网络全局异构性的不足,本文引入网络流的概念,综合考虑径向测度和中间测度,提出一种新的网络结构熵.特殊网络(如公用数据集Dolphins网络)的分析结果表明,本文提出的熵指标在一定程度上克服了其他网络熵指标的不足,更能够反映网络的真实拓扑结构;对随机网络、最近邻耦合网络、星型网络、无标度网络、Benchmark网络和小世界网络等典型网络的理论分析和仿真实验,进一步证明本文提出的熵指标在刻画一般复杂网络结构特征上的有效性和适用性.     

Analysis of robustness of urban bus network

In this paper,the invulnerability and cascade failures are discussed for the urban bus network.Firstly,three static models(bus stop network,bus transfer network,and bus line network) are used to analyse the structure and invulnerability of urban bus network in order to understand the features of bus network comprehensively.Secondly,a new way is proposed to study the invulnerability of urban bus network by modelling two layered networks,i.e.,the bus stop-line network and the bus line-transfer network and then the interactions between different models are analysed.Finally,by modelling a new layered network which can reflect the dynamic passenger flows,the cascade failures are discussed.Then a new load redistribution method is proposed to study the robustness of dynamic traffic.In this paper,the bus network of Shenyang City which is one of the biggest cities in China,is taken as a simulation example.In addition,some suggestions are given to improve the urban bus network and provide emergency strategies when traffic congestion occurs according to the numerical simulation results.     

一种基于点和边差异性的网络结构熵

熵是反映网络异构性的重要指标. 由于只是关注网络结构中"点"或"边"的单一作用,基于度分布和度相对值的两种传统熵在刻画网络结构特征时均存在缺陷. 文章综合考虑"点"和"边"差异性,定义一种新的网络结构熵,并对规则网络、随机网络和无标度网络等结构熵进行理论分析和仿真实验. 结果表明,这种新网络结构熵可以更有效地反映网络的结构特征,尤其是对于稀疏网络及星型网络的结构差异解释更为合理. 关键词： 均匀网络 无标度网络 熵     

一种新型电力网络局域世界演化模型

现实世界中的许多系统都可以用复杂网络来描述,电力系统是人类创造的最为复杂的网络系统之一.当前经典的网络模型与实际电力网络存在较大差异.从电力网络本身的演化机理入手,提出并研究了一种可以模拟电力网络演化规律的新型局域世界网络演化模型.理论分析表明该模型的度分布具有幂尾特性,且幂律指数在3—∞之间可调.最后通过对中国北方电网和美国西部电网的仿真以及和无标度网络、随机网络的对比,验证了该模型可以很好地反映电力网络的演化规律,并且进一步证实了电力网络既不是无标度网络,也不是完全的随机网络. 关键词： 电力网络 演化模型 局域世界 幂律分布     

基于网状光网络的智能实时监测保护系统的研究

目前光网络的拓扑结构越来越复杂,逐渐向网状结构光网络发展。提出了一种新的网状光网络智能实时在线监测保护方案,该方案采用下层嵌入式监测设备＋上层网管中心的模式。在嵌入式监测保护设备上加入保护光纤路由计算的功能,由嵌入式监测保护设备事先用路由算法计算出每条工作光纤的备用光纤路由,在工作光纤发生故障时及时进行切换。该方案实现了真正无人值守的光网络智能实时在线监测保护设备,适应网状光网络的发展要求,实现网状光网络的透明无阻断通信。     

Prediction based chaos control via a new neural network

In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network.     

Performance of a distributed WR-OBS control architecture

This paper proposes a new distributed wavelength-routed optical burst switching (WR-OBS) network architecture and two corresponding control protocols. By taking advantage of merits from both just enough time (JET) protocol and two-way signaling method, this new control architecture outperforms traditional JET OBS network in points of burst loss probability, system throughput and centralized WROBS network in network scalability confirmed by computer simulations. Further simulation is developed to compare the performance of the two control protocols, which leads to instructive discussion in real WR-OBS network design.     

异质自适应网络中的核心-边缘结构及其对疾病传播的抑制作用

节点属性异质自适应网络中疾病传播的研究表明节点属性异质性可以很大程度上增大传播阈值, 并且自组织形成一个更鲁棒的度异质网络结构. 本文从数值模拟方面研究鲁棒的度分布异质结构的自组织形成过程, 分析发现核心-边缘结构的形成才是导致传播阈值增大的根本原因. 鉴于此, 提出一种重连策略, 能够促进核心-边缘结构的形成, 从而达到增大传播阈值的目的. 这不仅有助于深入认识节点属性异质自适应网络中的流行病传播过程, 而且为疾病传播控制策略的提出提供了新思路.     

Advances in theoretical models of network science

In this review article, we will summarize the main advances in network science investigated by the CIAE Group of Complex Network in this field. Several theoretical models of network science were proposed and their topological and dynamical properties are reviewed and compared with the other models. Our models mainly include a harmonious unifying hybrid preferential model, a large unifying hybrid network model, a quantum interference network, a hexagonal nanowire network, and a small-world network with the same degree. The models above reveal some new phenomena and findings, which are useful for deeply understanding and investigating complex networks and their applications.      

Artificial Neural Network That Can Say “Unknown”

A new artificial neural network that can reject strange patterns is presented. The new network is an improved version of the RCE network. Each cell in the last layer of the present network has two thresholds. The new threshold produces the smallest boundary that encloses all examples associated with the cell in the pattern vector space. The present network can reject strange patterns by using this boundary. The rejection of strange patterns in the present and RCE networks was investigated by practical experiments using digits sampled from X-ray films of the human chest.     

Degree and wealth distribution in a network induced by wealth

A network induced by wealth is a social network model in which wealth induces individuals to participate as nodes, and every node in the network produces and accumulates wealth utilizing its links. More specifically, at every time step a new node is added to the network, and a link is created between one of the existing nodes and the new node. Innate wealth-producing ability is randomly assigned to every new node, and the node to be connected to the new node is chosen randomly, with odds proportional to the accumulated wealth of each existing node. Analyzing this network using the mean value and continuous flow approaches, we derive a relation between the conditional expectations of the degree and the accumulated wealth of each node. From this relation, we show that the degree distribution of the network induced by wealth is scale-free. We also show that the wealth distribution has a power-law tail and satisfies the 80/20 rule. We also show that, over the whole range, the cumulative wealth distribution exhibits the same topological characteristics as the wealth distributions of several networks based on the Bouchaud-Mèzard model, even though the mechanism for producing wealth is quite different in our model. Further, we show that the cumulative wealth distribution for the poor and middle class seems likely to follow by a log-normal distribution, while for the richest, the cumulative wealth distribution has a power-law behavior.     

A new complex network model and convergence dynamics for reputation computation in virtual organizations

In this Letter, a new complex network model is established for reputation computation in virtual organizations, and its dynamics is investigated. Several sufficient conditions have been derived to ensure the global asymptotical stability of the new complex network model by using Lyapunov method and linear matrix inequality (LMI) technique, where the stability means that the reputation degrees of entities can tend to some fixed constants as time evolves. Also, for this new model, some other dynamical phenomena such as bifurcation and chaos synchronization are proposed for our consideration in the future. This may open a new research branch in the area.     

一种无线传感器网络簇间拓扑演化模型及其免疫研究

基于复杂网络理论, 研究由于节点失效所导致的无线传感器网络性能下降的问题, 提出一种新的簇间拓扑演化模型, 在此基础上讨论病毒的免疫策略, 并给出一种新的免疫机理. 理论分析表明, 该模型演化生成的网络不仅具有较强的容错性, 而且还可以有效避免节点因能量很快耗尽而过早死亡. 研究还发现, 对于网络全局信息未知的情况, 与随机免疫和熟人免疫策略相比, 本文所提免疫策略能够获得较好的免疫效果. 通过数值仿真对理论分析进行验证.     

Degree distribution of a new model for evolving networks

We propose and study an evolving network model with both preferential and random attachments of new links, incorporating the addition of new nodes, new links, and the removal of links. We first show that the degree evolution of a node follows a nonhomogeneous Markov chain. Based on the concept of Markov chain, we provide the exact solution of the degree distribution of this model and show that the model can generate scale-free evolving network.     

大学物理实验网络教学平台的建构与应用

利用多媒体开发软件及网页制作工具,建构了一个大学物理实验网络教学平台。介绍了此平台的详细设计方案,以及它所具有的功能和特点,探索大学物理实验多媒体网络辅助教学的新应用。     

Evolution of a protein domain interaction network

In this paper, we attempt to understand complex network evolution from the underlying evolutionary relationship between biological organisms. Firstly, we construct a Pfam domain interaction network for each of the 470 completely sequenced organisms, and therefore each organism is correlated with a specific Pfam domain interaction network; secondly, we infer the evolutionary relationship of these organisms with the nearest neighbour joining method; thirdly, we use the evolutionary relationship between organisms constructed in the second step as the evolutionary course of the Pfam domain interaction network constructed in the first step. This analysis of the evolutionary course shows: (i) there is a conserved sub-network structure in network evolution; in this sub-network, nodes with lower degree prefer to maintain their connectivity invariant, and hubs tend to maintain their role as a hub is attached preferentially to new added nodes; (ii) few nodes are conserved as hubs; most of the other nodes are conserved as one with very low degree; (iii) in the course of network evolution, new nodes are added to the network either individually in most cases or as clusters with relative high clustering coefficients in a very few cases.     

Nonlinear Dynamical Behavior in BS Evolution Model Based on Small-World Network Added with Mechanism of Preferential Connection

In this paper, we introduce a modified small-world network added with new links with preferential connection instead of adding randomly, then we apply Bak-Sneppen (BS) evolution model on this network. Several dynamical character of the model such as the evolution graph, f₀ avalanche, the critical exponent D and τ, and the distribution of mutation times of all the nodes, show particular behaviors different from those of the model based on the regular network and the small-world network.     

Improving Associative Memory Function in the Steepest Descending Asynchronous Network with Tunneling Rule

In this paper effects of a new evolutionary rule added to the dynamics of the steepest descending asynchronous network model were studied. By numerical simulations, we found that the neural network operates very efficiently to improve the fault-tolerance when ,its capacity a ≤ 1 under the new rule. The simulations were conducted on both the low- and the highdimensional networks. A modified training scheme is also introduced.