Topological Structure of US Flight Network Based on Complex Network Theory

US flight network,composed of 285airports(nodes)and 3 971flights(edges)is studied.A static network model and a dynamic network model of US flight network are established.Firstly,the characteristics of static network are analyzed.One finds that such a network is a″small-world″and″scale-free″network.The cumulative degree distributions of weighted network and unweighted network follow″Double Pareto Law″.And the degree exponent of weighted network is smaller than unweighted network.The average shortest-path length is 2.368 9,which is smaller than previous results.The clustering coefficient of unweighted network is 0.637 1and of weighted network is 0.653 6,which are both bigger than previous results.The correlation of degree,unweighted clustering coefficient and weighted clustering coefficient are also discussed.Secondly,the characteristics of dynamic network are studied.The structure of flight network is changing as the time goes by on a day.In rush hours,the network′s character of″scale-free″is stronger than other times.And then the relationships of topological structures and congestion effects are addressed.

Multi-goal Control of Chaotic Connected Complex Networks

Beam transport network(BTN)with small world(SW)(so-called BTN-SW)and Lorenz chaotic connectednetwork with scale-free(SF)are taken as two typical examples,we proposed a global linear coupling and combined withlocal error feedback methods in sub-networks to realize multi-goal control method of halo and chaos in two networksabove.The simulation results show that the methods above is effective for any chaotic connected networks and has apotential of applications in based-halo-chaos secure communication.

Structures of semantic networks: how do we learn semantic knowledge

Global semantic structures of two large semantic networks, HowNet and WordNet, are analyzed. It is found that they are both complex networks with features of small-world and scale-free, but with special properties. Exponents of power law degree distribution of these two networks are between 1.0 and 2. 0, different from most scale-free networks which have exponents near 3.0. Coefficients of degree correlation are lower than 0, similar to biological networks. The BA （Barabasi-Albert） model and other similar models cannot explain their dynamics. Relations between clustering coefficient and node degree obey scaling law, which suggests that there exist self-similar hierarchical structures in networks. The results suggest that structures of semantic networks are influenced by the ways we learn semantic knowledge such as aggregation and metaphor.

The classification and analysis of dynamic networks

In this paper we, firstly, classify the complex networks in which the nodes are of the lifetime distribution. Secondly, in order to study complex networks in terms of queuing system and homogeneous Markov chain, we establish the relation between the complex networks and queuing system, providing a new way of studying complex networks. Thirdly, we prove that there exist stationary degree distributions of M-G-P network, and obtain the analytic expression of the distribution by means of Markov chain theory. We also obtain the average path length and clustering coefficient of the network. The results show that M-G-P network is not only scale-free but also of a small-world feature in proper conditions.

Science Letters:Model for cascading failures in congested Internet

Cascading failures often occur in congested networks such as the Internet. A cascading failure can be described as a three-phase process： generation, diffusion, and dissipation of the congestion. In this account, we present a function that represents the extent of congestion on a given node. This approach is different from existing fimctions based on betweenness centrality. By introducing the concept of ＇delay time＇, we designate an intergradation between permanent removal and nonremoval. We also construct an evaluation fimction of network efficiency, based on congestion, which measures the damage caused by cascading failures. Finally, we investigate the effects of network structure and size, delay time, processing ability and packet generation speed on congestion propagation. Also, we uncover the relationship between the cascade dynamics and some properties of the network such as structure and size.

Structures and dynamics of scientific knowledge networks:An empirical analysis based on a co-word network

Co-word networks are constructed with author-provided keywords in academic publications and their relations of co-occurrence.As special form of scientific knowledge networks,they represent the cognitive structure of scientific literature.This paper analyzes the complex structure of a co-word network based on 8,190 author-provided keywords extracted from 3,651 papers in five Chinese core journals in the field of management science.Small-world and scale-free phenomena are found in this network.A large-scale co-word network graph,which consists of one major giant component and many small isolated components,has been generated with the GUESS software.The dynamic growth of keywords and keyword co-occurrence relationships are described with four new informetrics measures.The results indicate that existing concepts always serve as the intellectual base of new ideas as represented by keywords.

复杂环境下工程物流网络级联失效抗毁性研究

合理评估工程物流网络抗毁性有助于保障工程物资及时准确供应,为制定物资运输计划与交通中断疏通应急预案提供科学支撑。针对复杂环境下工程物流网络运输通道承载能力不足且风险多样的特征,在考虑路段状态的基础上提出了拥堵效应分配策略,选择与复杂环境下工程物流网络中断成因相对应的三种攻击方式进行级联失效模拟。构建了反映路段抗毁能力、网络结构完整性及网络服务能力的评价指标,评估了复杂环境下工程物流网络和路段的抗毁性。最后,以复杂环境下某交通基础设施建设工程物流网络为例,验证了模型的有效性和合理性。结果表明:当路段容差参数β和失效阈值参数λ取值为0.5时,工程物流网络抗毁能力提升最为经济合理;工程物流网络中的路段在面临不同规模级联失效时存活率排序稳定,需要对存活率较低的路段制定针对性保通策略;工程物流网络抵抗介数降序蓄意攻击的能力较弱,面对风险降序蓄意攻击时抗毁性较强;相较于传统负载重分配策略,所提出的拥堵效应分配策略可以使工程物流网络抗毁性提升约38%。

A NEW CASCADING FAILURE MODEL WITH DELAY TIME IN CONGESTED COMPLEX NETWORKS

Cascading failures often occur in congested complex networks. Cascading failures can be expressed as a three-phase process： generation, diffusion, and dissipation of congestion. Different from the betweenness centrality, a congestion function is proposed to represent the extent of congestion on a given node. Inspired by the restart process of a node, we introduce the concept of ＂delay time,＂ during which the overloaded node Cannot receive or forward any traffic, so an intergradation between permanent removal and nonremoval is built and the flexibility of the presented model is demonstrated. Considering the connectivity of a network before and after cascading failures is not cracked because the overloaded node are not removed from network permanently in our model, a new evaluation function of network efficiency is also proposed to measure the damage caused by cascading failures. Finally, we investigate the effects of network structure and size, delay time, processing ability, and traffic generation speed on congestion propagation. Cascading processes composed of three phases and some factors affecting cascade propagation are uncovered as well.

Internet级联动力学分析与建模

不同于以往的介数模型,分析了Internet级联动力学特点,提出了节点拥塞函数,相当于给每个节点赋予一个动态权值,以表征该节点的拥塞程度.引入延迟时间的概念,在永久删除策略和不删除策略之间建立关联,以增强模型的灵活性.基于拥塞作用建立新的网络效率评估函数,并以此衡量级联故障的严重性.基于同质Statnet和异质Webgraph两种拓扑,研究不同的拓扑结构、规模、延迟时间、节点处理能力和包产生速率对拥塞传播的影响,揭示出级联故障传播分为3个阶段以及影响传播的部分主要因素.

基于网络拥塞的Internet级联故障建模

Internet是一个典型的具有自组织临界特性的复杂网络,分析了Internet级联动力学特点,指出了两点可能引发级联故障的原因;不同于以往的介数模型,提出了节点拥塞函数,相当于给每个节点赋一个动态的权值,以表征该节点的拥塞程度;加入了延迟时间,在永久删除策略和不删除策略之间建立关联.另外,建立了新的网络效率评估函数并以此衡量级联故障的严重性.仿真实验研究了不同的拓扑结构、规模、延迟时间、节点处理能力和包产生速率对拥塞传播的影响,揭示出级联故障传播分为3个阶段以及影响传播的主要因素.

Complexity of Public Transport Networks

The theory of complex networks was used to classify public transport networks into public transportation route networks, public transportation transfer networks, and bus station networks. The practical significance of the network parameters was then analyzed. The public transport networks in Langfang, Jining, and Dalian were then chosen as specific research cases. The results show that the public transportation networks have the characteristics of complex networks, In addition, the urban transportation network parameters all significantly affect the accessibility, convenience, and terrorist security capability of the urban public transportation network. The results link the findings with the actual situations to explore means to solve transportation system problems.

Detrended Fluctuation Analysis of Traffic Data

Different routing strategies may result in different behaviour of traffic on internet. We analyse the correlation of traffic data for three typical routing strategies by the detrended fluctuation analysis （DFA） and lind that the aregree of correlation of the data can be divided into three regions, i.e. weak, medium, and strong correlation. The DFA scalings are constants in both the regions of weak and strong correlations but monotonically increase in the region of medium correlation. We suggest that it is better to consider the traffic on complex network as three phases, i.e. the free, buffer, and congestion phase, than just as two phases believed before, i.e. the free and congestion phase.