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.     

无标度网络中最大传染能力限定的病毒传播问题研究

传统的病毒传播模型在无限大无标度网络上不存在病毒传播阈值,即无论病毒的传播速率多么低,病毒始终能够在网络中传播.但研究发现,这个结论是在网络中存在超级传染者的假设下得到的,然而许多真实的无标度网络中并不存在超级传染者.因此,文章提出了一个最大传染能力限定的病毒传播模型,并从理论上证明了在最大传染能力限定的无限大无标度网络上,病毒传播阈值是存在的;同时,也分析了最大传染能力限定下非零传播阈值与有限规模网络下非零传播阈值的本质区别,并解释了为什么人们总是认为传统病毒传播模型对许多真实网络病毒感染程度估计过高的 关键词： 无标度网络 最大传染能力 传播阈值 感染程度     

基于概率假设密度的无线传感器网络多目标跟踪算法

针对无线传感器网络多目标跟踪过程中杂波难以去除以及由数据关联复杂带来的计算复杂度高的问题,将概率假设密度滤波器应用于无线传感器网络,以更好地对多目标状态信息进行融合估计;首先,建立簇-树型无线传感器网络模型,并运用随机有限集理论对目标状态模型和传感器观测模型进行描述;然后,根据目标与节点之间的距离设置观测阈值,当传感器节点测量值小于观测阈值时,概率假设密度滤波器将实时对该组测量数据进行处理,从而实现传感器网络对目标状态的联合检测与跟踪;仿真结果表明,在无线传感器网络的多目标跟踪应用中,该算法比粒子滤波算法具有更高的跟踪效率和精度。     

有限气相反应速率条件下MFF模型应用方法的研究

本文使用"CO相对燃烧速率"的概念和通用的"有效影响参数转化方程",将带有气相反应速率无穷快假设的移动火焰锋面(MFF)模型成功地扩展应用到有限气相反应速率条件的计算中。对于多组不同有限气相反应速率、以及不同粒径的碳粒燃烧计算,MFF模型扩展应用的预报结果都与严格连续膜模型符合较好,碳粒着火温度的预报也更为准确。     

基于资源流行度的对等网络统计特征分析

对等网络体现出丰富的结构特征,如何深入认识更为精细的统计特征有待于进一步探索. 文章通过定义资源流行度阈值,建立基于资源流行度阈值的用户网络,体现对等网络中精细的结构特征. 针对一个具体的对等网络研究发现,基于低流行度资源形成的用户网络具备更加明晰的用户集群特性:随着资源流行度阈值的增大,分簇特征更为明显,且各簇内用户兴趣趋同性增强,不同簇间用户兴趣取向差异增大,用户分簇准确性提高. 更进一步,从各簇内用户的共享资源中提取基于资源粒度的低维簇指纹,该簇指纹可以在维度较低的情况下提供较高的表征精度. 关键词： 对等网络 流行度阈值 簇结构 簇指纹     

基于社交网络的观点传播动力学研究

社交网络和微博是重要的Web2.0应用模式, 其观点传播模式与其他网络媒体以及传统媒体相比有很大差异. 本文提出一种基于在线社交网络的观点传播模型, 研究社交网络中舆论观点扩散的形式与特征. 仿真结果表明: 模型中信息传播的速度与六度分割理论的结论十分符合; 一个带强烈倾向性的观点在固有观点均匀分布的网络中传播的情况下, 稳定时网络中不会出现相反的观点; 稳定时的观点分布与源节点的度和回溯深度有关, 并不受信任界限的限制, 这与Deffuant模型和Hegselmann-Krause模型不同. 同时, 本文还分析了传播意愿、观点变更率和信任界限对弛豫时间的影响.     

汉语语音识别中基于区分性权重训练的声调集成方法

提出一种区分性方法,将声调信息加入大词汇量连续语音识别系统中.该方法根据最小音子错误准则,区分性地训练模型相关的概率权重.利用这些权重对传统基于传统谱特征的隐马尔可夫模型概率以及声调模型概率进行加权,通过调整模型之间的作用程度提高系统识别率.推导了利用扩展Baum-Welch算法的权重更新公式.对不同模型权重组合策略进行了评估,并利用权重之间的平滑方法来克服权重训练过拟合的问题.分别通过大词汇连续语音的带调音节输出和汉字输出两种识别任务来验证区分性模型权重训练的性能.实验结果表明在两种识别任务上,区分性的模型权重较使用全局模型权重分别获得9.5%以及4.7%的相对误识率降低.这表明了区分性模型权重对提高声调集成性能的有效性.     

随机性对部分随机介质激光器阈值的影响

基于速率方程和Maxwell方程相结合的模型，采用时域有限差分法(FDTD)研究了介质的随机性和层数对部分随机介质激光器阈值的影响.模拟结果显示，当抽运速率超过阈值时，出现一个或者多个振荡模；随机性或者系统的尺度增加时，振荡模数量也增加；部分随机介质激光器的阈值在一定的随机强度和层数下将达到最小值，它与完全随机情况下的结论有所不同.对所得到的结论给出了物理解释.这些结果对于制作随机激光器和光集成潜在应用价值. 关键词： 激光物理 随机激光器 阈值 时域有限差分法     

一维氢原子团簇模型下的兆电子伏离子与库仑爆炸： 简化的数值模拟

本文使用一维双列氢原子团簇模型对强激光场中的团簇动力学过程进行了数值模拟。计算所得的质子最大动能和能谱均与他人的计算或实验结果符合较好,但计算大为简化。表明本文所述模型适用于氢原子团簇模拟。本文亦根据逸出质子数量随入射激光强度变化的规律总结出一个描述团簇库仑爆炸的光强阈值公式。     

利用羊八井广延大气簇射阵列探测来自壳型超新星遗迹G40.5-0.5的TeV Gamma射线发射

利用羊八井广延大气簇射阵列从2000年10月到2001年9月的数据,对壳型超新星 遗迹G40.5-0.5可能的TeV gamma射线发射进行了探测. 用对扩展源的二维分析方法发现一个最高超出为4.4σ的天区,EGRET不明源GeV J1907+0557非常接近 这一最高超出天区的中心.     

The evolution of opinions on scale-free networks

An opinion evolution model without “bounded confidence” is proposed in this paper. Computer simulation shows that our model can figure out the breakage of the coexistence of majority and minority after a period’s evolution. With further analysis, our model shows that, without the influence of the external field, the opinions will finally die out to a limited small value no matter what the initial condition of the system is. On the other hand, we simulate the evolution of the opinions under the influence of an external field, and get some meaningful and instructional results.     

Social bots and mass media manipulated public opinion through dual opinion climate

Opinion dynamics models based on the multi-agent method commonly assume that interactions between individuals in a social network result in changes in their opinions.However,formation of public opinion in a social network is a macroscopic statistical result of opinions of all expressive individuals(corresponding to silent individuals).Therefore,public opinion can be manipulated not only by changing individuals'opinions,but also by changing their states of expression(or silence)which can be interpreted as the phenomenon"spiral of silence"in social psychology.Based on this theory,we establish a"dual opinion climate"model,involving social bots and mass media through a multi-agent method,to describe mechanism for manipulation of public opinion in social networks.We find that both social bots(as local variables)and mass media(as a global variable)can interfere with the formation of public opinion,cause a significant superposition effect when they act in the same direction,and inhibit each other when they act in opposite directions.     

The spreading of opposite opinions on online social networks with authoritative nodes

The study of opinion dynamics, such as spreading and controlling of rumors, has become an important issue on social networks. Numerous models have been devised to describe this process, including epidemic models and spin models, which mainly focus on how opinions spread and interact with each other, respectively. In this paper, we propose a model that combines the spreading stage and the interaction stage for opinions to illustrate the process of dispelling a rumor. Moreover, we set up authoritative nodes, which disseminate positive opinion to counterbalance the negative opinion prevailing on online social networking sites. With analysis of the relationship among positive opinion proportion, opinion strength and the density of authoritative nodes in networks with different topologies, we demonstrate that the positive opinion proportion grows with the density of authoritative nodes until the positive opinion prevails in the entire network. In particular, the relationship is linear in homogeneous topologies. Besides, it is also noteworthy that initial locations of the negative opinion source and authoritative nodes do not influence positive opinion proportion in homogeneous networks but have a significant impact on heterogeneous networks. The results are verified by numerical simulations and are helpful to understand the mechanism of two different opinions interacting with each other on online social networking sites.     

Discrete Opinion Dynamics on Online Social Networks

This paper focuses on the dynamics of binary opinions {+1,-1} on online social networks consisting of heterogeneous actors.In our model,actors update their opinions under the interplay of social influence and selfaffirmation,which leads to rich dynamical behaviors on online social networks.We find that the opinion leading to the consensus features an advantage of the initially weighted fraction based on actors' strength over the other,instead of the population.For the role of specific actors,the consensus converges towards the opinion that a small fraction of high-strength actors hold,and individual diversity of self-affirmation slows down the ordering process of consensus.These indicate that high-strength actors play an essential role in opinion formation with strong social influence as well as high persistence.Further investigations show that the initial fraction of high-strength actors to dominate the evolution depends on the heterogeneity of the strength distribution,and less high-strength actors are needed in the case of a smaller exponent of power-law distribution of actors' strength.Our study provides deep insights into the role of social influence and self-affirmation on opinion formation on online social networks.     

基于无标度网络拓扑结构变化的舆论演化模型

基于BA无标度网络,构建了舆论和网络结构相互影响的自适应舆论演化模型.舆论的演化不仅受制于网络拓扑结构,而且也导致拓扑结构的变化.研究表明,演化达到稳态后,网络结构不再是初始的无标度网络而呈现泊松分布,而且随着时间的推移,系统中的舆论演化表现出很强的趋同效应,原来初始状态的几十个舆论,在长时间的演化后,大部分舆论灭亡,只有少数的舆论存留,且发展壮大.这种趋向与社会上的舆论、意见、信仰的演化大体上是符合的.     

基于新曝光冲突性消息的网络舆论逆转研究

对网络舆论逆转过程进行研究具有十分重要的意义,它有助于管理者有效引导舆论朝良性方向发展.目前,网络舆论逆转研究主要集中于动力学模型构建与仿真实验分析,其研究结果具有一定的理论价值.然而,这是否适用于真实社交网络环境,还尚未经过测试.为了对舆论逆转过程进行研究,构建了符合实际的模型,并对网络舆论逆转典型事例进行了深入分析.通过观察统计,发现网络舆论逆转具有自身的规律:新曝光冲突性消息是导致舆论发生逆转的根本原因;消息的传播影响着群体的发声与沉默;消息的属性包括传播率、可信度、观点倾向、起始传播时间和消息源中心度决定着舆论逆转的幅度.依据这一规律,设置了消息属性参数,并将消息传播与观点演化过程相结合,构建了网络舆论逆转模型.模型的仿真实验结果表明,新冲突性消息的传播率、可信度和消息源中心度正向影响着舆论逆转幅度,其中可信度较传播率影响更大.新的冲突性消息曝光的时间越早,舆论逆转的速率越快,幅度越大.该模型与实际相符,可为理解和解释网络舆论逆转过程、引导网络舆论提供理论依据.     

Non-consensus Opinion Models on Complex Networks

Social dynamic opinion models have been widely studied to understand how interactions among individuals cause opinions to evolve. Most opinion models that utilize spin interaction models usually produce a consensus steady state in which only one opinion exists. Because in reality different opinions usually coexist, we focus on non-consensus opinion models in which above a certain threshold two opinions coexist in a stable relationship. We revisit and extend the non-consensus opinion (NCO) model introduced by Shao et al. (Phys. Rev. Lett. 103:01870, 2009). The NCO model in random networks displays a second order phase transition that belongs to regular mean field percolation and is characterized by the appearance (above a certain threshold) of a large spanning cluster of the minority opinion. We generalize the NCO model by adding a weight factor W to each individual’s original opinion when determining their future opinion (NCOW model). We find that as W increases the minority opinion holders tend to form stable clusters with a smaller initial minority fraction than in the NCO model. We also revisit another non-consensus opinion model based on the NCO model, the inflexible contrarian opinion (ICO) model (Li et al. in Phys. Rev. E 84:066101, 2011), which introduces inflexible contrarians to model the competition between two opinions in a steady state. Inflexible contrarians are individuals that never change their original opinion but may influence the opinions of others. To place the inflexible contrarians in the ICO model we use two different strategies, random placement and one in which high-degree nodes are targeted. The inflexible contrarians effectively decrease the size of the largest rival-opinion cluster in both strategies, but the effect is more pronounced under the targeted method. All of the above models have previously been explored in terms of a single network, but human communities are usually interconnected, not isolated. Because opinions propagate not only within single networks but also between networks, and because the rules of opinion formation within a network may differ from those between networks, we study here the opinion dynamics in coupled networks. Each network represents a social group or community and the interdependent links joining individuals from different networks may be social ties that are unusually strong, e.g., married couples. We apply the non-consensus opinion (NCO) rule on each individual network and the global majority rule on interdependent pairs such that two interdependent agents with different opinions will, due to the influence of mass media, follow the majority opinion of the entire population. The opinion interactions within each network and the interdependent links across networks interlace periodically until a steady state is reached. We find that the interdependent links effectively force the system from a second order phase transition, which is characteristic of the NCO model on a single network, to a hybrid phase transition, i.e., a mix of second-order and abrupt jump-like transitions that ultimately becomes, as we increase the percentage of interdependent agents, a pure abrupt transition. We conclude that for the NCO model on coupled networks, interactions through interdependent links could push the non-consensus opinion model to a consensus opinion model, which mimics the reality that increased mass communication causes people to hold opinions that are increasingly similar. We also find that the effect of interdependent links is more pronounced in interdependent scale free networks than in interdependent Erd?s Rényi networks.     

Symmetry breaking in the opinion dynamics of a multi-group project organization

A bounded confidence model of opinion dynamics in multi-group projects is presented in which each group’s opinion evolution is driven by two types of forces:(i) the group’s cohesive force which tends to restore the opinion back towards the initial status because of its company culture;and(ii) nonlinear coupling forces with other groups which attempt to bring opinions closer due to collaboration willingness.Bifurcation analysis for the case of a two-group project shows a cusp catastrophe phenomenon and three distinctive evolutionary regimes,i.e.,a deadlock regime,a convergence regime,and a bifurcation regime in opinion dynamics.The critical value of initial discord between the two groups is derived to discriminate which regime the opinion evolution belongs to.In the case of a three-group project with a symmetric social network,both bifurcation analysis and simulation results demonstrate that if each pair has a high initial discord,instead of symmetrically converging to consensus with the increase of coupling scale as expected by Gabbay’s result(Physica A 378(2007) p.125 Fig.5),project organization(PO) may be split into two distinct clusters because of the symmetry breaking phenomenon caused by pitchfork bifurcations,which urges that apart from divergence in participants’ interests,nonlinear interaction can also make conflict inevitable in the PO.The effects of two asymmetric level parameters are tested in order to explore the ways of inducing dominant opinion in the whole PO.It is found that the strong influence imposed by a leader group with firm faith on the flexible and open minded follower groups can promote the formation of a positive dominant opinion in the PO.     

Bounded confidence and social networks

In the so-called bounded confidence model proposed by Deffuant et al, agents can influence each others opinion provided that the opinions are already sufficiently close enough. We discuss here the influence of possible social network topologies on the dynamics of this model.Received: 9 January 2004, Published online: 14 May 2004PACS: 89.65.-s Social and economic systems - 89.75.Fb Structures and organization in complex systems