Estimating and adjusting abnormal networks with unknown parameters and topology

The changes of parameters and topology in a complex network often lead to unexpected accidents in complex systems, such as diseases in neural systems and unexpected current in circuit system, so the methods of adjusting the abnormal network back to its normal conditions are necessary to avoid these problems. However, it is not easy to detect the structures and information of each network, even if we can find a network which has the same function as the abnormal network, it is still hard to use it as a reference to adjust the abnormal network because a lot of network information is unknown. In this paper, we design a "bridging network" as an information bridge between a normal network and an abnormal network to estimate and control the abnormal network. Through the "bridging network" and some adaptive laws, the abnormal parameters and connections in abnormal network can be adjusted to the same conditions as those of the normal network which is chosen as a reference model. Finally, the "bridging network" and the abnormal network achieve synchronization with the normal network. Besides, the detailed inner information in normal network and abnormal network can be accurately estimated by this "bridging network." Finally, the nodes in the abnormal network will behave normally after the correction. In this paper, we use Hindmarsh-Rose model as an example to describe our method.     

面向成本-收益好的无标度耦合网络构建方法

较大平均路径长度的网络会带来较大的网络延迟, 难以支持时间敏感业务与应用. 通过增加连接可以降低源和目的节点之间的跳数, 进而降低网络平均延迟, 使得更加快速地传播信息, 但是增加连接的同时也增加了网络构建成本. 分层网络是研究网络耦合的一个有效方法, 但目前网络构建过程中将每层网络分别处理并认为每层网络之间没有强相关性. 本文提出了一种面向成本-收益的无标度网络动态构建方法. 此方法将网络分为多层, 基于连续论在高层网络中添加连接, 使得网络演化为无标度网络. 此连续过程包括节点度增加过程和局部网络半径增长两个连续过程, 在增加连接的过程中引入表征网络构建成本和收益的成本-收益指标. 模拟结果表明引入成本-收益指标的无标度耦合网络构建方法能够在合理范围内有效降低网络平均路径长度, 提升网络性能, 并且本文给出了耦合网络的动态业务性能, 通过调整高层网络避免网络拥塞.     

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.     

一种具有正弦基函数权值的反馈型神经网络模型

提出了一种新的两层反馈型神经网络模型. 该网络采用正弦基函数作为权值, 神经元激活函数为线性函数, 连接形式为两层反馈型结构. 研究并定义了该反馈型神经网络的能量函数, 分析了网络运行的稳定性问题, 并证明了在Liapunov意义下网络运行的稳定性. 网络运行过程中, 其权值不做调整(但随时间按正弦规律变化), 网络状态不断地转换. 随着网络状态变化其能量不断减小, 最终在达到稳定时能量到达极小点. 由于该反馈型神经网络权值为正弦函数, 特别适合于周期信号的自适应逼近和检测, 为实际中周期性信号检测与处理提供了一种新的、有效的网络模型和方法. 作为应用实例把该网络应用于电力系统中电压凹陷特征量实时检测, 仿真结果表明, 网络用于信号检测不仅有很高的静态精度, 而且有非常好的动态响应特性.     

基于云计算和物联网的网络大数据技术研究

为了使网络大数据应用的范围更广,更大程度地提高网络数据存储与管理精度,减少网络数据处理与控制的时间,需要对网络大数据进行研究。当前的网络大数据研究方法多是采用Hadoop基础架构对网络大数据进行研究,在数据存储中没有设定具体的安全存储指标,无法得到数据安全存储指标权重,存在数据存储安全性能低,网络大数据研究精度偏差大等问题。为此,提出一种基于云计算和物联网的网络大数据研究方法。该方法首先利用分级网络编码对网络数据进行传输,以传输的数据为基础,采用CRC算法实现网络数据的计算,然后依据分组存储的方式将数据进行存储,最后利用分层逆序叠加定位法对网络数据进行高精度查询,由此完成对网络大数据的研究。实验结果表明,所提方法可以全面具体地对网络大数据进行研究,提高了数据处理精度和网络数据计算速度,增加了网络数据存储空间容量和查询效率,减少了网络数据运行时的丢失率,扩展了网络数据的运作范围,为后续网络大数据的研究提供了强有力的依据。     

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.     

基于通信序列熵的复杂网络传输容量

网络的传输性能在一定程度上依赖于网络的拓扑结构.本文从结构信息的角度分析复杂网络的传输动力学行为,寻找影响网络传输容量的信息结构测度指标.通信序列熵可以有效地量化网络的整体结构信息,为了表征网络整体传输能力,把通信序列熵引入到复杂网络传输动力学分析中,研究网络的通信序列熵与传输性能之间的关联特性,分析这种相关性存在的内在机理.分别在BA无标度和WS小世界网络模型上进行仿真,结果显示:网络的通信序列熵与其传输容量存在密切关联性,随着通信序列熵的增加,网络拓扑结构的均匀性随之增强,传输容量明显增加.网络的传输容量是通信序列熵的单调递增函数,与通信序列熵成正关联关系.通信序列熵可有效评估网络的传输容量,本结论可为设计高传输容量网络提供理论依据.     

Condensation and evolution of a space–time network

In this work, we try to propose in a novel way, using the Bose and Fermi quantum network approach, a framework studying condensation and evolution of a space–time network described by the Loop quantum gravity. Considering quantum network connectivity features in Loop quantum gravity, we introduce a link operator, and through extending the dynamical equation for the evolution of the quantum network posed by Ginestra Bianconi to an operator equation, we get the solution of the link operator. This solution is relevant to the Hamiltonian of the network, and then is related to the energy distribution of network nodes. Showing that tremendous energy distribution induces a huge curved space–time network may indicate space time condensation in high-energy nodes. For example, in the case of black holes, quantum energy distribution is related to the area, thus the eigenvalues of the link operator of the nodes can be related to the quantum number of the area, and the eigenvectors are just the spin network states. This reveals that the degree distribution of nodes for the space–time network is quantized, which can form space–time network condensation. The black hole is a sort of result of space–time network condensation, however there may be more extensive space–time network condensations, such as the universe singularity (big bang).     

Characteristics of microRNA co-target networks

The database of microRNAs and their predicted target genes in humans were used to extract a microRNA co-target network. Based on the finding that more than two miRNAs can target the same gene, we constructed a microRNA co-target network and analyzed it from the perspective of the complex network. We found that a network having a positive assortative mixing can be characterized by small-world and scale-free characteristics which are found in most complex networks. The network was further analyzed by the nearest-neighbor average connectivity, and it was shown that the more assortative a microRNA network is, the wider the range of increasing average connectivity. In particular, an assortative network has a power-law relationship of the average connectivity with a positive exponent. A percolation analysis of the network showed that, although the network is diluted, there is no percolation transition in the network. From these findings, we infer that the microRNAs in the network are clustered together, forming a core group. The same analyses carried out on different species confirmed the robustness of the main results found in the microRNA networks of humans.     

IP/GMPLS/Optical多层网络生存性机制的协调

通过对多层网络生存性问题、独立的网络生存性配置方案和多层网络生存性配置方案的研究分析,基于快速发展的GMPLS技术提出了IP/GMPLS/Optical多层网络结构,明确了多层网络生存性方案存在的问题,并利用GMPLS的统一控制平面进一步提出了基于层间协调机制的集成多层网络生存性方案,获得了优化的多层网络故障恢复能力.     

Synchronization of mobile chaotic agents on connected networks

This work studies the synchronization of a number of mobile agents on a substrate network. Each agent carries a chaotic map and randomly walks on a connected network. The collection of agents consists of another time-varying network derived from the substrate network. It is found that the synchronization conditions of this agent network depend on the average degree of the substrate network’s connectivity, the coupling strength between interacting agents, and the agent density in the network. Synchronization of the agent network on scale-free and ER networks is considered here, and it is found that the scale-free topology is more applicable to synchronize mobile chaotic agents. To get analytical insights, the star graph is taken and considered as a substrate network.     

Condensation and evolution of a space-time network

In this work, we try to propose in a novel way, using the Bose and Fermi quantum network approach, a framework studying condensation and evolution of a space-time network described by the Loop quantum gravity. Considering quantum network connectivity features in Loop quantum gravity, we introduce a link operator, and through extending the dynamical equation for the evolution of the quantum network posed by Ginestra Bianconi to an operator equation, we get the solution of the link operator. This solution is relevant to the Hamiltonian of the network, and then is related to the energy distribution of network nodes. Showing that tremendous energy distribution induces a huge curved space-time network may indicate space time condensation in high-energy nodes. For example, in the case of black holes, quantum energy distribution is related to the area, thus the eigenvalues of the link operator of the nodes can be related to the quantum number of the area, and the eigenvectors are just the spin network states. This reveals that the degree distribution of nodes for the space-time network is quantized, which can form space-time network condensation. The black hole is a sort of result of space-time network condensation, however there may be more extensive space-time network condensations, such as the universe singularity (big bang).      

一种基于离散数据从局部到全局的网络重构算法

网络的结构和功能彼此相互影响,网络上的功能往往体现为网络上的动力学过程,网络上的动力学过程通过网络中的行为表象数据进行体现.因此,根据网络上可观测的相关数据对网络结构进行重构将成为可能.本文拟解决如何根据网络上可观测的离散数据还原网络拓扑结构的问题,提出了在网络局部利用每一条离散数据对应节点的相似程度来推测节点间发生连边的可能性,通过多条离散数据重构网络各个局部拓扑并将由多条数据得到的局部拓扑进行叠加,最终重构出整个网络的全局拓扑结构的算法.为了验证算法的可行性与准确性,在小世界、无标度和随机网络中进行了网络重构实验,通过在三种不同类型及不同规模的网络中进行网络重构实验可以看出,网络重构算法在不同类型网络中的表现也不同,且网络的平均度值也会影响网络重构算法对数据的要求.为了验证算法的适用性,对三个实际网络进行了网络重构实验,结果显示算法能够适用实际较大规模网络的重构.该算法具有很好的适用性和准确度,适合不同类型网络的拓扑结构重构场景.     

Behaviours in a dynamical model of traffic assignment with elastic demand

This paper investigates the dynamical behaviour of network traffic flow. Assume that trip rates may be influenced by the level of service on the network and travellers are willing to take a faster route. A discrete dynamical model for the day-to-day adjustment process of route choice is presented. The model is then applied to a simple network for analysing the day-to-day behaviours of network flow. It finds that equilibrium is arrived if network flow consists of travellers not very sensitive to the differences of travel cost. Oscillations and chaos of network traffic flow are also found when travellers are sensitive to the travel cost and travel demand in a simple network.     

中国城市航空网络的实证研究与分析

以城市为节点，城市间直航线路为边，实证地研究了中国城市航空网络的拓扑性质.研究表明，中国城市航空网络是一个小世界网络，具有短的平均路径长度和大的簇系数，且其度分布服从双段幂律分布.它的度度相关性质与世界航空网络和北美航空网络都不相同.当度较小时，世界航空网络和北美航空网络都是正相关的，但中国城市航空网络未表现出度度相关性；而对于度较大的节点，世界航空网络中其邻点平均度几乎是一个常值，但中国城市航空网络却呈现出负相关性.以往的实证研究暗示，节点具有明确几何位置的网络，如计算机互联网、电力网络等，不表现层次性.但是中国城市航空网络展现出明显的层次性，表明地理因素对其结构演化的影响并不强烈.进一步地，以城市间直航计划每周提供的座位数为边权，研究了网络的含权性质，发现该网络节点度权之间是幂律相关的，相关指数为1.37.     

OPNET和STK联合的空间光骨干网络仿真设计

空间光骨干网络是我国空间信息网络的核心。为了对空间光骨干网络中的协议体系、网络架构和关键技术进行可行性验证，开发了基于OPNET和STK(satellite tool kit，卫星工具包)联合的空间光骨干网络仿真平台。设计了由4颗GEO卫星组成的天基骨干网、由10颗LEO卫星组成的天基接入网；开发了由物理层到应用层的光网络协议仿真模型和LEO、GEO、地面站3种节点进程模型，对光骨干网络进行了仿真测试。仿真结果表明:空间光骨干网络最大时延为5.48 s，最小为0.35 s，网络全局平均时延为1.06 s，地面站实时吞吐量最大值为0.88 Gb/s，最小为0.3 Gb/s，证明该平台可准确模拟空间光骨干网络功能，满足空间业务高效传输的需求，为空间光骨干网络性能验证提供仿真平台支持。     

A Path-Based Distribution Measure for Network Comparison

As network data increases, it is more common than ever for researchers to analyze a set of networks rather than a single network and measure the difference between networks by developing a number of network comparison methods. Network comparison is able to quantify dissimilarity between networks by comparing the structural topological difference of networks. Here, we propose a kind of measures for network comparison based on the shortest path distribution combined with node centrality, capturing the global topological difference with local features. Based on the characterized path distributions, we define and compare network distance between networks to measure how dissimilar the two networks are, and the network entropy to characterize a typical network system. We find that the network distance is able to discriminate networks generated by different models. Combining more information on end nodes along a path can further amplify the dissimilarity of networks. The network entropy is able to detect tipping points in the evolution of synthetic networks. Extensive numerical simulations reveal the effectivity of the proposed measure in network reduction of multilayer networks, and identification of typical system states in temporal networks as well.     

Chaotic diagonal recurrent neural network

We propose a novel neural network based on a diagonal recurrent neural network and chaos,and its structure and learning algorithm are designed.The multilayer feedforward neural network,diagonal recurrent neural network,and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map.The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks.     

中国铁路客运网网络性质的研究

中国铁路客运系统可以采用两种不同的网络构建方式来描述. 一种是以铁路的站点作为“节点”，并以轨道作为“边”，这样生成的网络称为铁路地理网. 统计显示该网络的平均群聚系数〈C〉近似为零，故该网络为树状网络. 另一种是以站点作为“节点”，任意两个站点间只要有同一列车在这两个站点停靠，就可以认为这两个站点间有连线，这样生成的网络称为车流网. 统计显示该网络有较大的平均群聚系数和较小的平均网络距离〈d〉，而且该网络节点的度分布基本上服从无标度幂律分布，故车流网为具有无标度性质的小世界网络. 关键词： 铁路地理网 车流网 小世界 无标度分布     

Topology of the Erasmus student mobility network

The collaboration network generated by the Erasmus student mobilities in the year 2003 is analyzed and modeled. Nodes of this bipartite network are European universities and links are the Erasmus mobilities between these universities. This network is a complex directed and weighted graph. The non-directed and non-weighted projection of this network does not exhibit a scale-free nature, but proves to be a small-word type random network with a giant component. The connectivity data indicates an exponential degree distribution, a relatively high clustering coefficient and a small radius. It can be easily modeled by using a simple configuration model and arguing the exponential degree distribution. The weighted and directed version of the network can also be described by means of simple random network models.