基于遗传算法的重复囚徒困境博弈策略在复杂网络中的演化

利用遗传算法研究重复囚徒困境博弈策略在复杂网络中的演化.研究结果表明：处于复杂网络中有记忆的个体通过基因的复制、重组、变异和选择能够进化出一种自组织的合作机制.这种合作机制既能够在群体中激发合作行为的产生，加强和维护持续的合作行为，同时又能对背叛的个体进行惩罚和报复，因此能够促使复杂网络中进化出具有很高合作率的群体. 关键词： 复杂网络 遗传算法 进化博弈 合作     

基于复杂网络理论的微博用户关系网络演化模型研究

微博给人们提供便利的同时也产生了较大的负面影响.为获取微博谣言的传播规律,进而采取有效措施防控其传播,本文基于复杂网络理论研究微博用户关系网络的内部特征,提出一种微博用户关系网络演化模型,借助于平均场理论,分析该演化模型的拓扑统计特性,以及谣言在该演化模型上的传播动力学行为.理论分析和仿真实验表明,由该模型演化生成的微博用户关系网络具有无标度特性.度分布指数不仅与反向连接概率有关,而且还取决于节点的吸引度分布.研究还发现,与指数分布和均匀分布相比,当节点吸引度满足幂律分布时,稳态时的谣言传播程度较大.此外,随着反向连接概率或节点初始连边数量的增加,谣言爆发的概率以及网络中最终接受谣言的节点数量都会明显增大.     

零行列式策略在雪堆博弈中的演化

零行列式策略不仅可以单方面设置对手收益,而且可以对双方的收益施加一个线性关系,从而达到敲诈对手的目的.本文针对零行列式策略博弈前期与稳态期的收益存在偏差,基于Markov链理论给出零行列式策略与全合作策略博弈的瞬态分布、瞬态收益及达到稳态所需时间.发现在小的敲诈因子下,敲诈者前期收益高于稳态期收益,敲诈因子较大时,情况截然相反,并且敲诈因子越大,越不利于双方合作,达到稳态也越慢.这为现实生活中频繁更新策略的博弈提供了一种计算实时收益的方法.此外针对敲诈策略与进化人的博弈,论证了双方均背叛状态下,进化人下次博弈时一定进化为全合作策略.通过对所有状态下策略更新过程仿真,发现进化人在四种情况下的进化速度有显著差异,并最终演化为全合作策略,表明零行列式策略是合作产生的催化剂.     

基于信息熵的社交网络观点演化模型

随着网络服务的发展,社交网络逐渐成为信息传播的新媒介.因此,研究网络舆情演化具有重要意义和实用价值.为了更好地研究网络舆论,在信息熵的基础上,提出了一个社交网络观点演化模型.此模型存在以下两个特点:一是可以反映个体面对正负两种观点趋向做出抉择时的心理过程;二是可以反映个体形成新观点时主观因素和客观因素的影响.在仿真实验中,讨论了舆论环境对个体观点演化的影响,初始观点和自信度对观点演化的影响,以及意见领袖对群体观点演化的影响.实验结果表明,该模型可以反映真实社交网络中个体的心理学特征,比如个体的观点形成会受到舆论环境的影响,自信的个体不愿意接受他人的观点,当意见领袖存在时群体的观点会受到影响等.     

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.     

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.     

基于演化博弈的社交网络模型演化研究

社会网络研究的兴起,为网络演化规律研究提供了有效工具,但大多数研究集中从宏观机制评估网络演化的动态过程. 本文基于公共品博弈,通过演化博弈与网络拓扑共演化方式,从微观角度提出了多社区动态网络演化模型（dMCPGG）. 即以节点间演化博弈为动力,修改节点间边的关系,驱动网络拓扑演化. 考虑到网络异质性,采用基于拓扑势的偏好规则更准确全面的描述节点影响力. 通过数值模拟和仿真实验,验证了本模型的合理性,不仅重现了无标度网络及随机网络的节点度、聚类系数及平均路径长度的结构特性,还准确捕捉到真实社交网络的演化过程. 关键词： 公共品博弈 拓扑势 偏好选择 共演化     

具有异质增益因子的超图上的演化公共品博弈

近年来,空间结构和社会多样性对群体合作演化的影响吸引了人们极大的关注.本文在空间公共品博弈中引入了异质增益因子,研究超图上合作行为的演化.除所有博弈群组具有相同增益因子的原始模型外,还考虑了包括均匀分布、指数分布和幂律分布在内的不同异质性强度的三类异质增益因子分布.数值模拟结果显示,上述4种增益因子分布对应的公共品博弈中,均匀随机超图阶数g的增大有利于提高群体的合作水平.更进一步地,对比超图上原始空间公共品博弈的演化结果,群体中博弈群组增益因子异质性的引入能够显著促进群体的合作行为,幂律分布情况下能够使得系统获得最高合作水平.此外,研究了超边数目对群体合作演化的影响,结果表明上述结论对超边数目鲁棒,并且超边数目L的增大会抑制公共品博弈中合作行为的涌现.一定程度上,本文的研究结果有助于我们更好地理解超图上空间公共品博弈的演化动力学.     

双椭圆界面Richtmyer-Meshkov流动中的相互干扰效应

实验研究了在马赫数为1.18的平面激波冲击作用下,双椭圆界面RichtmyerMeshkov不稳定性演化的动力学过程。椭圆短轴b与入射激波方向垂直,通过改变双椭圆的中心间距d,采用片光高速摄影和PIV(粒子图像测速)技术,观测了4种不同情形的演化模态,获得了界面演化多幅像和700μs时刻的速度场,分析了双椭圆气柱之间的相互干扰效应。当d/b为4.0或3.0时,相互干扰效应较弱,双椭圆气柱演化为两个反向旋转的对涡,速度极大值接近30m/s,出现在2个位置,速度最小值几乎为零,出现在4个位置。当d/b为2.0或1.2时,相互干扰效应很强,两个内涡完全消失,双椭圆气柱演化为一个反向旋转的涡对结构,速度极大值出现在4个位置,速度极小值出现在两个位置。d/b=2.0时,界面演化图像与圆形气柱演化过程类似。相比d/b=2.0的情形,d/b=1.2时产生更大的斜压涡量,界面演化发展更快,后期出现二次涡现象和分叉结构,整体结构类似于单椭圆气柱演化过程。当d/b在2.0~3.0之间变化时,存在一个是否形成两个内涡的非线性临界值。针对双气柱界面演化明显的内涡弱化现象,分析了4种可能的机制。     

超网络中标度律的涌现

本文构建超网络和复杂网络中统一演化模型,研究超网络无标度特性演化机理和拓扑性质.利用Poisson过程理论和连续化方法对模型进行分析,获得网络稳态平均超度分布的解析表达式.仿真实验和理论分析相符合.结果表明:随着网络规模的增大,这个动态演化网络的超度分布遵循无标度的特性.它不仅将每次增加一个新节点与若干个老节点围成一条超边的超网络模型和每次增加若干个新节点与一个老节点围成一条超边的超网络模型统一在一个模型中,而且将复杂网络中著名的无标度模型也作为我们模型的特例.     

Prisoner’s Dilemma Game with Cooperation-Defection Dominance Strategies on Correlational Multilayer Networks

As multilayer networks are widely applied in modern society, numerous studies have shown the impact of a multilayer network structure and the network nature on the proportion of cooperators in the network. In this paper, we use Barabási–Albert scale-free networks (BA) and Watts and Strogatz networks (WS) to build a multilayer network structure, and we propose a new strategy-updating rule called “cooperation-defection dominance”, which can be likened to dominant and recessive traits in biogenetics. With the newly constructed multilayer network structure and the strategy-updating rules, based on the simulation results, we find that in the BA-BA network, the cooperation dominance strategy can make the networks with different rs show a cooperative trend, while the defection dominance strategy only has an obvious effect on the network cooperation with a larger r. When the BA network is connected to the WS network, we find that the effect of strategy on the proportion of cooperators in the network decreases, and the main influencing factor is the structure of the network. In the three-layer network, the cooperation dominance strategy has a greater impact on the BA network, and the proportion of the cooperators is enhanced more than under the natural evolution strategy, but the promotion effect is still smaller than that of the two-layer BA network because of the WS network. Under the defection dominance strategy, the WS layer appears different from the first two strategies, and we conclude through simulation that when the payoff parameter is at the middle level, its cooperator proportion will be suppressed, and we deduce that the proportion of cooperators and defectors, as well as the payoff, play an important role.     

Promotion of cooperation induced by the interplay between structure and game dynamics

We consider the coupled dynamics of the adaption of network structure and the evolution of strategies played by individuals occupying the network vertices. We propose a computational model in which each agent plays a n-round Prisoner's Dilemma game with its immediate neighbors, after that, based upon self-interest, partial individuals may punish their defective neighbors by dismissing the social tie to the one who defects the most times, meanwhile seek for a new partner at random from the neighbors of the punished agent. It is found that the promotion of cooperation is attributed to the entangled evolution of individual strategy and network structure. Moreover, we show that the emerging social networks exhibit high heterogeneity and disassortative mixing pattern. For a given average connectivity of the population and the number of rounds, there is a critical value for the fraction of individuals adapting their social interactions, above which cooperators wipe out defectors. Besides, the effects of the average degree, the number of rounds, and the intensity of selection are investigated by extensive numerical simulations. Our results to some extent reflect the underlying mechanism promoting cooperation.     

Spatial Games Based on Pursuing the Highest Average Payoff

We propose a strategy updating mechanism based on pursuing the highest average payoff to investigate the prisoner＇s dilemma game and the snowdrift game. We apply the new rule to investigate cooperative behaviours on regular, small-world, scale-free networks, and find spatial structure can maintain cooperation for the prisoner＇s dilemma game. fn the snowdrift game, spatial structure can inhibit or promote cooperative behaviour which depends on payoff parameter. We further study cooperative behaviour on scale-free network in detail. Interestingly, non-monotonous behaviours observed on scale-free network with middle-degree individuals have the lowest cooperation level. We also find that large-degree individuals change their strategies more frequently for both games.     

Adaptive bridge control strategy for opinion evolution on social networks

In this paper, we present an efficient opinion control strategy for complex networks, in particular, for social networks. The proposed adaptive bridge control (ABC) strategy calls for controlling a special kind of nodes named bridge and requires no knowledge of the node degrees or any other global or local knowledge, which are necessary for some other immunization strategies including targeted immunization and acquaintance immunization. We study the efficiency of the proposed ABC strategy on random networks, small-world networks, scale-free networks, and the random networks adjusted by the edge exchanging method. Our results show that the proposed ABC strategy is efficient for all of these four kinds of networks. Through an adjusting clustering coefficient by the edge exchanging method, it is found out that the efficiency of our ABC strategy is closely related with the clustering coefficient. The main contributions of this paper can be listed as follows: (1) A new high-order social network is proposed to describe opinion dynamic. (2) An algorithm, which does not require the knowledge of the nodes' degree and other global∕local network structure information, is proposed to control the "bridges" more accurately and further control the opinion dynamics of the social networks. The efficiency of our ABC strategy is illustrated by numerical examples. (3) The numerical results indicate that our ABC strategy is more efficient for networks with higher clustering coefficient.     

复杂网络的顶点着色及其在疾病免疫中的应用

在复杂网络研究中, 对于网络结构特征的分析已经引起了人们的极大关注, 而其中的网络着色问题却没有得到足够的重视. 为了理解网络结构与着色之间的关系, 本文研究了WS, BA网络以及不同宏观结构参量对于正常K色数的影响, 发现最大团数可以大致反映正常K色数的变化趋势, 而网络的平均度和匹配系数比异质性和聚类系数对于色数的影响更大. 对于一些实际网络的正常着色验证了本文的分析结果. 对复杂网络的顶点进行着色后, 根据独立集内任意两个顶点均不相邻的特点, 我们提出了基于独立集的免疫策略. 与全网随机免疫相比, 基于独立集的免疫策略可令网络更为脆弱, 从而有效抑制疾病的传播. 基于网络着色的独立集提供了一种崭新的免疫思路, 作为一个简单而适用的平台,有助于设计更为有效的免疫策略. 关键词： 复杂网络 正常着色 独立集 免疫策略     

Emergent hierarchical structures in multiadaptive games

We investigate a game-theoretic model of a social system where both the rules of the game and the interaction structure are shaped by the behavior of the agents. We call this type of model, with several types of feedback couplings from the behavior of the agents to their environment, a multiadaptive game. Our model has a complex behavior with several regimes of different dynamic behavior accompanied by different network topological properties. Some of these regimes are characterized by heterogeneous, hierarchical interaction networks, where cooperation and network topology coemerge from the dynamics.     

Cooperation and community structure in social networks

Situations of conflict giving rise to social dilemmas are widespread in society. One way of studying these important phenomena is by using simplified models of individual behavior under conflicting situations such as evolutionary game theory. Starting from the observation that individuals interact through networks of acquaintances, we study the evolution of cooperation on model and real social networks through well known paradigmatic games. Using a new payoff scheme which leaves replicator dynamics invariant, we find that cooperation is sustainable in such networks, even in the difficult case of the prisoner’s dilemma. The evolution and stability of cooperation implies the condensation of game strategies into the existing community structures of the social network in which clusters of cooperators survive thanks to their higher connectivity towards other fellow cooperators.     

Time scales in evolutionary game on adaptive networks

Most previous studies concerning spatial games have assumed strategy updating occurs with a fixed ratio relative to interactions. We here set up a coevolutionary model to investigate how different ratio affects the evolution of cooperation on adaptive networks. Simulation results demonstrate that cooperation can be significantly enhanced under our rewiring mechanism, especially with slower natural selection. Meanwhile, slower selection induces larger network heterogeneity. Strong selection contracts the parameter area where cooperation thrives. Therefore, cooperation prevails whenever individuals are offered enough chances to adapt to the environment. Robustness of the results has been checked under rewiring cost or varied networks.     

基于传播免疫的复杂网络可控性研究

复杂网络控制反映了人类对复杂系统的认识深度和改造能力. 最新研究成果基于线性系统控制理论建立了复杂网络可控性的理论架构, 能够发现任意拓扑结构的线性时不变复杂网络中控制全部节点状态的最小驱动节点集, 但是该模型未考虑免疫节点或失效节点对控制信号传播的阻断.在继承该模型优点的前提下, 重新构建了基于传播免疫的复杂网络控制模型.在采用分属于随机免疫和目标免疫两种策略的 4个方法确定免疫节点的情况下,分析14个真实网络的可控性.结果表明:如果将网络中度数、 介数和紧密度指标较高的节点作为免疫节点,将极大地提高控制复杂网络的难度. 从而在一定程度上丰富了以往模型的结论.     

Cooperative Dynamics in Lattice-Embedded Scale-Free Networks

We investigate cooperative behaviors of lattice-embeddedscale-free networking agents in the prisoner's dilemma game model byemploying two initial strategy distribution mechanisms, which are specificdistribution to the most connected sites (hubs) and random distribution. Ourstudy indicates that the game dynamics crucially depends on the underlyingspatial network structure with different strategy distribution mechanism.The cooperators' specific distribution contributes to an enhanced level ofcooperation in the system compared with random one, and cooperation isrobust to cooperators' specific distribution but fragile to defectors' specific distribution. Especially, unlike the specific case, increasing heterogeneity of network does not always favor the emergence of cooperation under random mechanism. Furthermore, we study the geographical effects and find that the graphically constrained network structure tends to improve the evolution of cooperation in random case and in specific one for a large temptation to defect.