一种无线传感器网络健壮性可调的能量均衡拓扑控制算法

无线传感器网络中, 应用环境的干扰导致节点间距不能被准确度量. 所以利用以节点间距作为权重的闭包图(EG)模型构建的拓扑没有考虑环境的干扰, 忽略了这部分干扰带来的能耗, 缩短了网络生存时间. 针对无线传感器网络拓扑能量不均的特点和EG模型的缺陷, 首先引入节点度调节因子, 建立通信度量模型和节点实际生存时间模型; 其次量化网络节点度, 从而获取满足能量均衡和网络生命期最大化需求的节点度的取值规律; 然后利用该取值规律和函数极值充分条件解析推导出网络最大能量消耗值和最长生存时间, 并获得最优节点度; 最后基于以上模型提出一种健壮性可调的能量均衡拓扑控制算法. 理论证明该拓扑连通且为双向连通. 仿真结果说明网络能利用最优节点度达到较高的健壮性, 保证信息可靠传输, 且算法能有效平衡节点能耗, 提高网络健壮性, 延长网络生命周期.     

国际石油价格复杂网络的动力学拓扑性质

为了分析国际石油价格波动的变化特征,依据粗粒化方法,将1986年1月2日至2009年11月17日美国西得克萨斯原油现货逐日离岸价格转化为由3个表示涨落字符{R,e,D}构成的102种5字串组成的价格波动状态为网络节点(即连续5天的价格波动组合),按照时间顺序连边,构建原油价格有向加权波动网络,用复杂网络拓扑结构记录原油价格波动周涨落的信息,计算网络的度与度分布、聚类系数、最短路径长度等动力学统计量.结果表明,石油价格序列的波动网络节点度与累积度分布、节点度值与秩呈幂律分布,前32个节点(模态)的节点度值比较大,且这些石油价格波动模态中都包含表示价格变化上升趋势的字符.原油价格波动网络部分节点的中介中心性能力较强,24.5%的节点承担了网络80.97%的中介中心性功能,平均路径长度2.285,距离是2和3的情况占总数的86.8%,从网络结构拓扑性质这一新的角度验证了原油价格变化的复杂特征.这些对于我们识别具有拓扑统计重要性的节点模态,理解原油价格波动的内在规律和价格变化信息的传导有一定指导意义.     

虚拟网络行为对互联网整体特性的影响

虚拟网络是一种依赖Internet基础设施所提供的传输能力，但又具有独立拓扑结构和信息传递规则的应用层网络行为逻辑网络.提出了耦合虚拟网络行为与物理节点的抽象模型，对一类典型的虚拟网络逻辑拓扑给互联网整体特性带来的影响进行了分析.研究表明在虚拟网络作用下，节点数据包排队长度存在相变特性，但相变临界点比对规则网络发生了明显左移，网络性能相对恶化.当数据包注入速率小于相变临界速率时，节点数据包排队长度不相关或短程相关；在接近临界速率处，节点数据包排队长度长程相关，幂指数H增大，网络获得更强的长程相关性.同时，在注入速率大于或等于临界速率时，虚拟网络行为使网络呈现出一致的长程相关特性.     

多种形式的加权广义Farey组织网络金字塔的复杂性

构建了三类确定性加权广义Farey组织的网络金字塔.理论推导并数值计算了网络金字塔的拓扑性质(度分布、平均最短路径、平均聚类系数和相称性系数等),进而将Farey序列作为网络节点的确定、随机和混合的三种权重值,以此为基础计算并拟合了三类网络金字塔的点的强度分布和边的权重分布.计算结果初步揭示了加权广义Farey组织的网络金字塔的复杂性特征,有助于了解一些实际网络的复杂性和多样性.     

基于二分图的温度网络拓扑性质研究

利用中国地区435个台站1961—2002年逐日平均温度序列,将温度变化发生在9天时间尺度上的特征编码在网络中,通过研究二分图温度网络(BGT网络)中节点与项目的关系,揭示出9天时间尺度上温度变化的特征及其在空间上的拓扑统计性质.网络中各节点RRRD, RrDD, eeed, DRRD, DDRR等所代表的温度波动模态在网络中异常频发,对9天尺度温度变化的预报有一定的指导意义.统计网络的节点度分布,集群系数等拓扑结构特征量,发现BGT网络服从正态分布特征.BGT网络项目内节点度的多样性大体上 关键词： 二分图温度网络 气候系统 拓扑结构     

基于随机矩阵的金融网络模型

随机矩阵理论运用于金融领域中研究金融相关系数矩阵的相关性,相关系数矩阵是网络构建中的关键因素,本文将随机矩阵理论与网络构建相结合,研究基于随机矩阵的金融网络模型.本文选取上海证券市场的股票数据,将其中的股票数据分成四个阶段,基于随机矩阵理论,讨论金融相关系数矩阵和随机矩阵的特征值统计性质,并在此基础上对现有的去噪方法进行改进,建立更适合构建金融网络的相关系数矩阵,并构建金融网络模型.然后,基于随机矩阵理论和网络的关键节点分析比较去噪前后的金融网络以及噪声网络,发现对网络去噪后仍保留了原始网络的关键重要的信息,而噪声信息对应的是原始网络中度比较小的节点所代表的信息.最后,基于去噪网络,分析金融网络的拓扑结构,如最小生成树、模体和社团结构,发现改进后的金融网络的拓扑性质更加明显,结构更加紧密.     

利用复杂网络研究中国温度序列的拓扑性质

依据粗粒化方法,将中国1961—2002年逐日平均温度序列转化为由5个特征字符{R,r,e,d,D}构成的温度符号序列.以符号序列中的125种3字串组成的温度波动模态为网络的节点(即连续4d的温度波动组合),并按照时间顺序连边,构建有向加权的温度波动网络,进而将温度波动模态间的相互作用等综合信息蕴含于网络的拓扑结构之中.对随机序列和Lorenz系统的混沌序列分别构建随机和混沌波动网络.计算三种网络的度与度分布、聚类系数、最短路径长度等动 关键词： 气候变化 气候复杂网络 拓扑结构     

开放式复杂航空网络系统的动力学演化

本文首先实证研究了中国航空网(CAN)与外部经济环境,即国民生产总值(GDP)的相关性,进而揭示CAN的演化方式及其拓扑特征的起源.发现自1998年起网络节点的度与其GDP成线性关系,表明了网络拓扑与经济因素有密切的联系,且CAN的度演化服从类似于多重过程(multiplicative process)的模式,而该模式又强烈依赖于GDP的增长率.这种独特的模式暗示了CAN具有类似适应度模型的微观机理.通过对演化方程的研究发现:1)CAN在增长过程中,节点加入的时刻具有经济上的偏好性,即节点加入的时刻与该节 关键词： 国民生产总值(GDP) 中国航空网(CAN) 双段幂率分布 多重过程     

基于Web 2.0的边与节点同时增长网络模型

模拟了Web2.0网络的发展过程并研究其拓扑结构,分析某门户网站实际博客数据的度分布、节点度时间变化,发现与先前的无标度网络模型有所差别.根据真实网络的生长特点,提出了边与节点同时增长的网络模型,包括随机连接及近邻互联的网络构造规则.仿真研究表明,模拟的网络更接近实际,在没有优先连接过程时,模型能得到幂率的度分布;并且网络有更大的聚类系数以及正的度相关性。     

基于复杂网络理论的北京公交网络拓扑性质分析

为分析公交复杂网络的拓扑性质, 本文以北京市为例, 选取截止到2010年7月的北京全市(14区、2县)的1165条公交线路和9618个公交站点为样本数据, 运用复杂网络理论构建起基于邻接站点的有向加权复杂网络模型. 该方法以公交站点作为节点, 相邻站点之间的公交线路作为边, 使得网络既具有复杂网络的拓扑性质同时节点(站点)又具有明确的地理坐标. 对网络中节点度、点强度、强度分布、平均最短路径、聚类系数等性质的分析显示, 公交复杂网络的度和点强度分布极为不均, 网络中前5%和前10%节点的累计强度分布分别达到22.43%和43.02%; 点强度与排列序数、累积强度分布都服从幂律分布, 具有无标度和小世界的网络特点, 少数关键节点在网络中发挥着重要的连接作用. 为分析复杂网络中的关键节点, 本文通过承载压力分析和基于"掠夺" 的区域中心节点提取两种方法, 得到了公交复杂网络中两类不同表现的关键节点. 这些规律也为优化城市公交网络及交通规划发展提供了新的参考建议.     

Vertex centrality as a measure of information flow in Italian Corporate Board Networks

The aim of this article is to investigate the governance models of companies listed on the Italian Stock Exchange by using a network approach, which describes the interlinks between boards of directors. Following mainstream literature, I construct a weighted graph representing the listed companies (vertices) and their relationships (weighted edges), the Corporate Board Network; I then apply three different vertex centrality measures: degree, betweenness and flow betweenness. What emerges from the network construction and by applying the degree centrality is a structure with a large number of connections but not particularly dense, where the presence of a small number of highly connected nodes (hubs) is evident.Then I focus on betweenness and flow betweenness; indeed I expect that these centrality measures may give a representation of the intensity of the relationship between companies, capturing the volume of information flowing from one vertex to another. Finally, I investigate the possible scale-free structure of the network.     

A network analysis of the Chinese stock market

In many practical important cases, a massive dataset can be represented as a very large network with certain attributes associated with its vertices and edges. Stock markets generate huge amounts of data, which can be use for constructing the network reflecting the market’s behavior. In this paper, we use a threshold method to construct China’s stock correlation network and then study the network’s structural properties and topological stability. We conduct a statistical analysis of this network and show that it follows a power-law model. We also detect components, cliques and independent sets in this network. These analyses allows one to apply a new data mining technique of classifying financial instruments based on stock price data, which provides a deeper insight into the internal structure of the stock market. Moreover, we test the topological stability of this network and find that it displays a topological robustness against random vertex failures, but it is also fragile to intentional attacks. Such a network stability property would be also useful for portfolio investment and risk management.     

Evolution of the Internet AS-level topology:From nodes and edges to components

Studying the topology of infrastructure communication networks(e.g., the Internet) has become a means to understand and develop complex systems. Therefore, investigating the evolution of Internet network topology might elucidate disciplines governing the dynamic process of complex systems. It may also contribute to a more intelligent communication network framework based on its autonomous behavior. In this paper, the Internet Autonomous Systems(ASes) topology from 1998 to 2013 was studied by deconstructing and analysing topological entities on three different scales(i.e., nodes,edges and 3 network components: single-edge component M1, binary component M2 and triangle component M3). The results indicate that: a) 95% of the Internet edges are internal edges(as opposed to external and boundary edges); b) the Internet network consists mainly of internal components, particularly M2 internal components; c) in most cases, a node initially connects with multiple nodes to form an M2 component to take part in the network; d) the Internet network evolves to lower entropy. Furthermore, we find that, as a complex system, the evolution of the Internet exhibits a behavioral series,which is similar to the biological phenomena concerned with the study on metabolism and replication. To the best of our knowledge, this is the first study of the evolution of the Internet network through analysis of dynamic features of its nodes,edges and components, and therefore our study represents an innovative approach to the subject.     

基于物联网的设施温室监控系统中无线传感器节点设计

设计具有低成本、低功耗、易扩展的设施温室监控设备,深入研究无线网络构成,优化网络结构、降低组网难度、提高组网效率。采用 MSP430F149为核心的处理器;采用CC2530射频芯片实现无线传输;提供多种协议接口方便传感器的扩展,给出了土壤温湿度、空气温湿度、光照传感器的选型和指标参数;由本设计的无线传感器节点作为WSN中的全功能设备同时充当路由节点,搭建了MESH型的无线传感器网络结构。经测试,通过硬件设计及网络结构搭建,系统具有结构简单、组网快捷、数据传输可靠性高等优点,能较好的满足无线传感器节点在设施温室监控中的应用。     

Distinguishing manipulated stocks via trading network analysis

Manipulation is an important issue for both developed and emerging stock markets. For the study of manipulation, it is critical to analyze investor behavior in the stock market. In this paper, an analysis of the full transaction records of over a hundred stocks in a one-year period is conducted. For each stock, a trading network is constructed to characterize the relations among its investors. In trading networks, nodes represent investors and a directed link connects a stock seller to a buyer with the total trade size as the weight of the link, and the node strength is the sum of all edge weights of a node. For all these trading networks, we find that the node degree and node strength both have tails following a power-law distribution. Compared with non-manipulated stocks, manipulated stocks have a high lower bound of the power-law tail, a high average degree of the trading network and a low correlation between the price return and the seller-buyer ratio. These findings may help us to detect manipulated stocks.     

Extracting hidden fluctuation patterns of Hang Seng stock index from network topologies

We present a model of complex network generated from Hang Seng index (HSI) of Hong Kong stock market, which encodes stock market relevant both interconnections and interactions between fluctuation patterns of HSI in the network topologies. In the network, the nodes (edges) represent all kinds of patterns of HSI fluctuation (their interconnections). Based on network topological statistic, we present efficient algorithms, measuring betweenness centrality (BC) and inverse participation ratio (IPR) of network adjacency matrix, for detecting topological important nodes. We have at least obtained three uniform nodes of topological importance, and find the three nodes, i.e. 18.7% nodes undertake 71.9% betweenness centrality and closely correlate other nodes. From these topological important nodes, we can extract hidden significant fluctuation patterns of HSI. We also find these patterns are independent the time intervals scales. The results contain important physical implication, i.e. the significant patterns play much more important roles in both information control and transport of stock market, and should be useful for us to more understand fluctuations regularity of stock market index. Moreover, we could conclude that Hong Kong stock market, rather than a random system, is statistically stable, by comparison to random networks.     

The impact of sampling density upon cortical network analysis: regions or points

The choice of representation has a fundamental influence on the network analysis results of an empirical data set. The answers to two basic questions - how to define a node and how to define an edge between a pair of nodes - are not obvious in the network analysis of brain imaging data. We considered the first question in the case of magnetic resonance imaging (MRI)-based cortical thickness networks. We selected network nodes to represent vertices of a cortical surface mesh or cortical brain regions. The first network represents the maximal level of detail available in the analysis of cortical thickness networks, while the latter network represents the typical level of detail in the current network analysis studies. We compared the network analysis results between these two representations. The basic network measures behaved approximately as expected when the level of detail increased. However, the overall connectivity of nodes was greater in the vertex level, degree of clustering was smaller in the vertex level, and the node centralities were different between the levels. Further, many parameters of vertex-level network were more robust to the selection of the correlation threshold used to define the edges of network. We conclude that albeit many qualitative network properties were consistent between the two resolution levels, the vertex-level resolution revealed details that were not visible at the regional-level networks, and this additional detail could be useful for some applications. Finally, a similar methodology as the one used here could be used to study effects of the sampling density in other brain-imaging-based networks, for example, in resting-state functional MRI.     

Voltage distribution in growing conducting networks

We investigate by random-walk simulations and a mean-field theory how growth by biased addition of nodes affects flow of the current through the emergent conducting graph, representing a digital circuit. In the interior of a large network the voltage varies with the addition time s < t of the node as V(s) ∼ ln(s)/s ^θ when constant current enters the network at last added node t and leaves at the root of the graph which is grounded. The topological closeness of the conduction path and shortest path through a node suggests that the charged random walk determines these global graph properties by using only local search algorithms. The results agree with mean-field theory on tree structures, while the numerical method is applicable to graphs of any complexity. Received 26 August 2002 Published online 29 November 2002     

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.