基于超图的超网络相继故障分析

分析了快递超网络和电子元件超网络的相继故障扩散方式, 结合超图理论提出了2-section 图分析法和线图分析法, 并仿真分析了无标度超网络耦合映像格子的相继故障进程. 结果表明: 无标度超网络对外部攻击表现出了既鲁棒又脆弱的特性. 针对相继故障的不同扩散方式, 无标度超网络的相继故障行为表现出不同的特点. 超网络的相继故障行为和超网络的超度以及超边度分布有密切的联系, 也和超网络中超边的个数有关. 通过和同规模的Barabasi-Albert (BA)无标度网络对比, 在同一种攻击方式下同规模的无标度超网络都比BA 无标度网络表现出了更强的鲁棒性. 另外, 基于超边扩散的相继故障进程比基于节点扩散的相继故障进程更加缓慢.     

一类无标度随机图的度序列

本文从-个新的角度对-类随机图的度序列进行了分析.证明了此模型度分布的存在性,得到了网络规模比较大的情况下度为七的节点所占比例数的表达式.此外,我们还将模型扩展到每个时间步增加边数为随机变量的情形,得到了类似的结论.     

一门崭新的交叉科学:网络科学(下篇)

在上篇综述里,我们评述了网络科学的发展简史,基本概念和网络分类,以及国内外迄今提出的复杂网络的主要理论模型及其拓扑特性。着重总结了我们"一院二校"开展的国家自然科学基金重点项目的研究进展,上篇的研究成果主要涉及加权网络模型、和谐统一的混合模型和量子信息网络模型及其纳米网络,简单说,我们探索了从宏观网络到微观网络的不同层次的若干特点和结果。在下篇综述里,我们将系统概述5个重要课题的研究进展,从第7章到第11章,课题内容包括:复杂网络的动力学完全同步与网络的拓扑结构之间的关系;网络拓扑结构的对称性破缺导致动力学部分同步;给出判断给定网络部分同步斑图稳定性的方法和一般判据,讨论了由网络的多种拓扑对称导致的部分同步斑图的竞争和选择问题以及李指数谱的简并性质。进一步,分别以具有小世界和无标度拓扑结构的束流输运网络为典型的"混沌复杂网络",提出了实现束晕-混沌的同步和控制的若干方法,特别是实现分区网络的多目标的同步控制问题。同时,基于Vicsek模型和Boid模型,研究了生物体集群动态网络的拓扑结构和动力学方程,提出复杂多智能体网络的协调控制策略。另外,从复杂网络的不同拓扑结构对合作涌现和资源分配的作用角度,评述了三个主要的博弈模型—少数者博弈、囚徒困境和雪堆博弈,研究了复杂网络的群聚系数和网络异质性等结构特性对个体的博弈行为的重要影响。最后第12章,我们简介其它课题的进展,讨论了网络科学面临的挑战,并展望了其应用与发展前景。     

Chemical synaptic coupling-induced delay-dependent synchronization transitions in scale-free neuronal networks

Chemical synaptic couplings are more common than electric(gap junction) connections in neurons.In this paper,the firing synchronizations induced by chemical synaptic coupling in chemically delayed scale-free networks of modified Hodgkin-Huxley neurons have been studied.It was found that the chemical coupling-induced synchronization transitions are delay-dependent and much different for various delay lengths.In the absence of delay,the neurons exhibit a transition from chaotic bursting(CB) to bursting synchr...     

Evolution of donations on scale-free networks during a COVID-19 breakout

Having a large number of timely donations during the early stages of a COVID-19 breakout would normally be considered rare. Donation is a special public goods game with zero yield for donors, and it has the characteristics of the prisoners' dilemma. This paper discusses why timely donations in the early stages of COVID-19 occurred. Based on the idea that donation is a strategy adopted by players during interconnection on account of their understanding of the environment, donation-related populations are placed on social networks and the inter-correlation structures in the population are described by scale-free networks. Players in donation-related populations are of four types: donors, illegal beneficiaries, legal beneficiaries, and inactive people. We model the evolutionary game of donation on a scale-free network. Donors, illegal beneficiaries and inactive people learn and update strategies under the Fermi update rule, whereas the conversion between legal beneficiaries and the other three types is determined by the environment surrounding the players. We study the evolution of cooperative action when the agglomeration coefficient, the parameters of the utility function, the noise intensity, the utility coefficient, the donation coefficient and the initial states of the population on the scale-free network change. For population sizes of 50, 100, 150, and 200, we give the utility functions and the agglomeration coefficients for promoting cooperation and study the corresponding steady states and structural characteristics of the population. We identify the best ranges of the noise intensity K, the donation coefficient α and the utility coefficient β for promoting cooperation at different population sizes. Furthermore, with the increase of the population size, the donor traps are found. At the same time, it is discovered that the initial states of the population have a great impact on the steady states; thus the upper and lower triangle phenomena are proposed. We also find that the population size itself is also an important factor for promoting donation, pointing out the direction of efforts to further promote donation and achieve better social homeostasis under the donation model.     

一类高聚类系数的加权无标度网络及其同步能力分析

针对真实世界中大规模网络都具有明显聚类效应的特点, 提出一类具有高聚类系数的加权无标度网络演化模型, 该模型同时考虑了优先连接、三角结构、随机连接和社团结构等四种演化机制. 在模型演化规则中, 以概率p增加单个节点, 以概率1–p增加一个社团. 与以往研究的不同在于新边的建立, 以概率φ在旧节点之间进行三角连接, 以概率1–φ进行随机连接. 仿真分析表明, 所提出的网络度、强度和权值分布都是服从幂律分布的形式, 且具有高聚类系数的特性, 聚类系数的提高与社团结构和随机连接机制有直接的关系. 最后通过数值仿真分析了网络演化机制对同步动态特性的影响, 数值仿真结果表明, 网络的平均聚类系数越小, 网络的同步能力越强. 关键词： 无标度网络 加权网络 聚类系数 同步能力     

An Evolving Random Network and Its Asymptotic Structure

In this paper, we propose an evolving random network. The model is a linear combination of preferential attachment model and uniform model. We show that scaling limit distribution of the number of leaves at time $n$ is approximated by normal distribution and the proportional degree sequence obeys power law. The branching structure and maximum degree are also discussed in this paper.     

Epidemic spreading on a scale-free network with awareness

This paper presents a modified susceptible-infected-recovered(SIR) model with the effects of awareness and vaccination to study the epidemic spreading on scale-free networks based on the mean-field theory.In this model,when susceptible individuals receive awareness from their infected neighbor nodes,they will take vaccination measures.The theoretical analysis and the numerical simulations show that the existence of awareness and vaccination can significantly improve the epidemic threshold and reduce the risk of virus outbreaks.In addition,regardless of the existence of vaccination,the awareness can increase the spreading threshold and slow the spreading speed effectively.For a given awareness and a certain spreading rate,the total number of infections reduces with the increasing vaccination rate.     

具有非均匀传输和抗攻击差异的网络病毒传播模型

针对实际网络中节点存在抗攻击差异以及边的非均匀传输等情况,基于平均场理论,提出具有抗攻击差异和非均匀传输特性的网络病毒传播平均场SIR模型.该模型中,通过引入脆弱性函数和传输函数,分别描述节点的抗攻击差异以及边的非均匀传输能力.通过对所提模型的分析,得到传播阈值的理论结果.理论分析和仿真表明,节点的抗攻击差异以及边的非均匀传输,都可导致出现正的传播阈值,使得病毒传播风险有效降低.     

复杂网络中考虑不完全免疫的病毒传播研究

复杂网络中不完全免疫包括免疫失败和免疫失效两种情况,本文研究两者同时存在对网络病毒传播行为的影响,基于平均场理论,提出一种新的传播模型.理论分析表明,免疫失败和免疫失效同时存在显著降低了网络的传播临界值,增强了病毒的感染程度.根据传播临界值与免疫节点密度、免疫成功率以及免疫失效率之间的关系,给出有效控制网络病毒传播的策略.通过数值仿真进行验证。