复杂网络及其新近研究进展简介

简单地介绍了近10年来蓬勃发展的复杂网络研究新领域, 特别是其中最具代表性的随机网络、小世界网络和无尺度网络模型及其基本参数和概念, 并简要地回顾了复杂网络理论在生物和大脑神经网络、流行病传播与免疫控制、交通与社会经济、无线通讯、计算机和互联网、传感器网络、语言学与社会科学以及群体活动和编队飞行问题中的分散控制、稳定性特别是一致性问题等方面的应用趋势.     

The Effect of Queueing Strategy on Network Traffic

In recent years, the transportation system has been faced by increasing challenge in congestion and inefficiency, and research in traffic network has become a significant area of interest. In this paper, we introduce a dynamic-information-based (DIB) queueing strategy into network traffic model under the efficient routing strategy. DIB makes a packet with higher priority to be delivered if there are less packets travelling along its path from the current node to the destination. It is found that, compared with the traditional first-in-first-out (FIFO) queueing strategy, DIB can effectively balance the traffic load of the system via delaying packets to be delivered to congested nodes. Although the network capacity has no obvious changes, some other indexes which reflect transportation efficiency are efficiently improved in the congestion state. Besides, extensive simulation results and discussions are provided to explain the phenomena. The results may provide novel insights for research on traffic systems.     

增长及非增长无标度网络的成因解析

分析两类无标度网络的形成原因,提出一个无标度网络演化模型并进行一系列数值实验.基于分析和实验得到推论:只要保持足够低的网络密度,通过基于度的偏好连接就可形成长期稳定的无标度网络.规模增长和点边增删既是客观存在,又起到了控制网络密度的作用,足够低的网络密度和基于度的偏好连接是所有无标度网络共同的必要条件.推论可同时解释增长和非增长无标度网络的形成原因.研究结果有助于理解各种真实无标度网络和建立相应的模型.     

复杂网络研究概述

近年来，真实网络中小世界效应和无标度特性的发现激起了物理学界对复杂网路的研究热潮．复杂网络区别于以前广泛研究的规则网络和随机网络最重要的统计特征是什么?物理学家研究复杂网络的终极问题是什么?物理过程以及相关的物理现象对拓扑结构是否敏感?物理学家进入这一研究领域的原因和意义何在?复杂网络研究领域将来可能会向着什么方向发展?文章围绕上述问题，从整体上概述了复杂网络的研究进展．     

具有局部结构的增长无标度网络中传染病传播机制研究

为了研究人群中的一些基本的社会关系结构,如家庭、室友、同事等,对传染病传播过程的影响机制,本文建立了一个具有局部结构的增长无标度网络模型.研究表明,局部结构的引入使得该网络模型能够同时再现社会网络的两个重要特征：节点度分布的不均匀性以及节点度之间的相关性.首先,该网络的节点度和局部结构度均服从幂律分布,且度分布指数依赖于局部结构的大小.此外,局部结构的存在还导致网络节点度之间具有正相关特性,而这种正相关正是社会网络所特有的一个重要特性.接着,通过理论分析和数值模拟,我们进一步研究了该网络结构对易感者-感染 关键词： 复杂网络 无标度网络 局部结构 传染病建模     

Weighted Scaling in Non-growth Random Networks

We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in non-growth random networks. In this model, we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it. The network evolves according to a vertex strength preferential selection mechanism. During the evolution process, the network always holds its total number of vertices and its total number of single-edges constantly. We show analytically and numerically that a network will form steady scale-free distributions with our model. The results show that a weighted non-growth random network can evolve into scale-free state. It is interesting that the network also obtains the character of an exponential edge weight distribution. Namely, coexistence of scale-free distribution and exponential distribution emerges.     

Degree and connectivity of the Internet’s scale-free topology

This paper theoretically and empirically studies the degree and connectivity of the Internet's scale-free topology at an autonomous system (AS) level. The basic features of scale-free networks influence the normalization constant of degree distribution p(k). It develops a new mathematic model for describing the power-law relationships of Internet topology. From this model we theoretically obtain formulas to calculate the average degree, the ratios of the k_min-degree (minimum degree) nodes and the k_max-degree (maximum degree) nodes, and the fraction of the degrees (or links) in the hands of the richer (top best-connected) nodes. It finds that the average degree is larger for a smaller power-law exponent λ and a larger minimum or maximum degree. The ratio of the k_min-degree nodes is larger for larger λ and smaller k_min or k_max. The ratio of the k_max-degree ones is larger for smaller λ and k_max or larger k_min. The richer nodes hold most of the total degrees of Internet AS-level topology. In addition, it is revealed that the increased rate of the average degree or the ratio of the k_min-degree nodes has power-law decay with the increase of k_min. The ratio of the k_max-degree nodes has a power-law decay with the increase of k_max, and the fraction of the degrees in the hands of the richer 27% nodes is about 73% (the '73/27 rule'). Finally, empirically calculations are made, based on the empirical data extracted from the Border Gateway Protocol, of the average degree, ratio and fraction using this method and other methods, and find that this method is rigorous and effective for Internet AS-level topology.     

Generalized minimum information path routing strategy on scale-free networks

This paper presents a new routing strategy by introducing a tunable parameter into the minimum information path routing strategy we proposed previously.It is found that network transmission capacity can be considerably enhanced by adjusting the parameter with various allocations of node capability for packet delivery.Moreover,the proposed routing strategy provides a traffic load distribution which can better match the allocation of node capability than that of traditional efficient routing strategies,leading to a network with improved transmission performance.This routing strategy,without deviating from the shortest-path routing strategy in the length of paths too much,produces improved performance indexes such as critical generating rate,average length of paths and average search information.     

Convergence speed of consensus problems over undirected scale-free networks

Scale-free networks and consensus behaviour among multiple agents have both attracted much attention.To investigate the consensus speed over scale-free networks is the major topic of the present work.A novel method is developed to construct scale-free networks due to their remarkable power-law degree distributions,while preserving the diversity of network topologies.The time cost or iterations for networks to reach a certain level of consensus is discussed,considering the influence from power-law parameters.They are both demonstrated to be reversed power-law functions of the algebraic connectivity,which is viewed as a measurement on convergence speed of the consensus behaviour.The attempts of tuning power-law parameters may speed up the consensus procedure,but it could also make the network less robust over time delay at the same time.Large scale of simulations are supportive to the conclusions.     

Self-organized Criticality in a Modified Evolution Model on Generalized Barabási-Albert Scale-Free Networks

A modified evolution model of self-organized criticality on generalized Barabási-Albert (GBA) scale-free networks is investigated. In our model, we find that spatial and temporal correlations exhibit critical behaviors. More importantly, these critical behaviors change with the parameter b, which weights the distance in comparison with the degree in the GBA network evolution.