基于关系HK模型的群体观点演化建模与仿真

针对传统HK模型中,未考虑个体间人际关系对观点阈值外个体观点交互的影响,提出一种关系HK模型,以研究关系度对群体观点演化时间和系统观点簇的影响,以及关系度在两个意见观点趋向相反的观点领袖及其支持者中发挥的作用。研究结果表明,群体中关系度的存在有利于观点达成共识,缩短系统观点演化时间。特别地,在具有观点领袖的群体中,观点领袖支持者的特征因素对系统观点演化结果起着决定性的作用,如支持者初始观点、观点阈值、关系度阈值和初始观点坚持度。当群体中存在两个意见趋向相反且彼此间没有关系度的观点领袖时,系统最终出现分裂状态,而观点领袖及其支持者之间关系度的存在,有利于系统观点达成共识。     

面向论坛网络的观点传播与舆情形成模型

根据网络论坛的信息传播规律和用户交互特性,提出了一个基于节点影响力的离散时间观点传播与舆情形成模型。在该模型中,节点具有自我影响力和观点值,并且节点影响力被定义为网络中边的权值,节点在每一个周期内重新分配权值并更新它们的观点。节点观点收敛于某一定值时,形成网络舆情。仿真结果表明,所提模型可以准确地体现节点观点的动态交互特性和收敛特征,并可为舆情传播趋势的预测研究提供依据。     

基于观点认同的群体情绪模型

为了能够有效地模拟群体环境中群体情绪的演化过程, 以情绪感染为理论基础, 提出一种新的基于观点认同的群体情绪模型。该模型引入个体观点值、情绪触发阈值来实现观点交互与认同对群体情绪演化所产生的影响作用。观点交互采用有界信任Deffuant模型来实现。通过模拟发现, 观点交互不仅加速了群体情绪融合过程, 而且在多观点群体环境中对群体情绪融合态势也具有决定性的作用。实验模拟结果表明, 本模型不仅能够体现情绪感染过程中个体的无意识、自动感染过程, 而且也能够体现个体对传染对象的选择性过程, 弥补了情绪感染过程中个体无认知的缺陷。     

基于关系度的群体观点演化模型与仿真*

针对传统的连续观点动力学模型中,缺乏对观点传播过程中人与人之间关系的考虑,通过扩展Hegselmann-Krause模型,引入关系度和关系阈值,研究个体间关系程度对群体观点演化的影响以及关系度在具有观点领袖的团体中所发挥的作用。研究结果表明：在关系阈值的影响范围内,关系度有利于群体观点达成共识并能够缩短系统观点的收敛时间;另外,观点领袖的支持者越多且支持者初始观点越接近观点领袖的观点,观点演化的收敛时间越短,系统平衡时的观点值越接近观点领袖的初始观点值。     

基于从众行为的改进Hegselmann-Krause观点动力学分析

在许多社交场景中,个体往往因为从众行为而改变自己的观点.为了探究从众行为对个体观点演化的影响,提出具有群体压力的改进Hegselmann-Krause(HK)模型.与已有模型不同,所提出模型中每个个体对其余个体的影响程度并不总是相同.首先,在此基础上,通过理论分析该模型的收敛性,并给出该模型的收敛时间及其与信任边界、群体压力大小等因素的关系;然后,考虑到群体中存在合作和对抗2种关系,进一步提出具有群体压力的改进符号HK模型,验证该模型的收敛性,并给出其收敛时间以及与信任边界、群体压力大小等因素的关系;最后,仿真分析信任边界和群体压力对所提出2种模型的观点演化过程和收敛时间的影响,并验证所提出模型的有效性.结果表明,信任边界与群体压力越大,群体内个体的观点收敛越快.     

基于网民心理的微博舆论传播模型及仿真研究*

根据社交网络中微博舆论的传播特点,考虑到不同心理倾向的网民对舆论观点和传播特征的不同,建立了基于网民心理的微博舆论传播模型,并通过网民的心理倾向将网民分为理性和非理性个体,研究了有向社交网络中,不同群体下舆论观点的形成和传播特征,以及辟谣者发布的辟谣信息对网民观点的影响。仿真结果表明,与理性群体相比,群体中非理性个体的存在加速了信息的扩散与传播,并且在信息可信度较低的情况下,刺激群体产生大量与初始传播者意见相反的观点。研究还表明,网络事件的辟谣信息,发布时间越早,可信度越高,该信息发挥的作用就越大。另外,当辟谣信息发布不及时或者可信度不高时,群体中非理性个体会加剧该信息的反作用。     

考虑社会网络邻接关系的群体判断矩阵决策方法

随着社会化媒体的快速发展,社会化因素已经成为影响群体决策过程及其结果的重要因素.针对群体决策者的判断信息以残缺判断矩阵形式给出,且考虑群体决策者社会网络邻接关系的群体决策问题,提出可行的解决方法.首先,提出一种基于决策者相似性程度和社会网络距离的残缺判断矩阵补全方法;然后,提出考虑决策者社会网络影响力的群体共识交互决策模型,该交互模型不仅考虑群体决策者之间的社会邻接关系,而且可以在较大程度上保存决策者给定的原始判断信息;最后,通过一个物流企业选择存储仓库的算例验证所提出算法的可行性和优势.     

社会网络中的影响力综述

在社会影响力传播领域,社会网络作为媒介在社会个体之间相互影响、传播信息与观点方面发挥着根本性的作用。首先讨论了社会影响力的定义,以及社会影响力作为一种社会相关性的本质属性;然后分析阐述了影响力最大化问题中的独立级联模型和线性阈值模型以及能够确定具有影响力个体的贪心算法和探索式算法;最后对影响力研究的新趋势,诸如基于社区结构的影响力最大化算法以及讨论多个主题、多种行为的影响力研究进行了分析。     

社交网络中舆论共识形成的动态模型分析

本研究致力于深入探讨社交网络中舆论形成的复杂过程, 尤其是关注去中心化环境下达成共识的机制. 研究提出了一种新的意见分类策略, 即第二置信区间. 该策略旨在对传统DeGroot共识模型进行改进, 从而发展出两种不同的意见动态模型: FAI模型和ORA模型. 这些模型综合考虑了个体对周围意见的接受程度和重视程度, 并通过对社交网络中邻域意见的深入分析, 对个体模型进行了全面设置, 涵盖私人意见、表达意见、固执度以及偏好等多重因素. 研究结果表明, 在特定参数设定下, FAI模型和ORA模型均能比原DeGroot模型更加迅速地达成共识. 具体来说, ORA模型的收敛速度在700步长左右, 而FAI模型的收敛速度随参数值的增加而逐步接近ORA模型. 相较于基准模型, ORA模型在收敛意见值上的差异较小, 不超过3.5%, 而FAI模型则显示出更大的波动性. 这些发现不仅加深了对于社交网络中公共意见形成机制的理解, 也强调了个体邻域内意见动力学在共识形成过程中的重要性, 为此领域的未来研究提供了新的视角和研究方向.     

基于社交网络的群体信任算法

社交网络(Social Network Service,SNS)中群体之间的信任关系是广泛存在的,目前大部分信任模型的研究基本只涉及一对一之间的个体信任关系,并没有充分考虑一对多、多对一以及多对多之间的群体信任关系。在现有信任模型研究的基础上,给出一种群体信任算法。该算法根据社交网络中群体的特点,将交互的两个群体抽象为两个群体节点,将复杂的多种信任关系转化为一对一之间的信任关系,从而对群体间的信任关系进行描述和度量。仿真结果表明,该算法能合理地度量与计算每个节点在群体中的可信度以及群体间的直接信任度。     

An adaptive group decision making framework: Individual and local world opinion based opinion dynamics

Opinion dynamics (OD) models, which simulate individuals’ opinion evolution process on social network to analyze the final state of opinion distribution in a group, usually differ from each other due to the differences in social network evolution rules and opinion evolution rules. However, most existing social network evolution rules and opinion evolution rules usually cannot characterize the comprehensive influence of key factors such as neighbors and opinion differences in social relationships. To fully consider the properties of social network evolution and improve the efficiency of consensus reaching process in group decision making, this paper introduces the concept of local world opinion derived from individuals’ common friends, and then proposes an individual and local world opinion-based OD model. In the proposed model, social network evolution is jointly determined by the distance between individual opinions and network structure similarity. The pair of individuals with the largest consensus improvement space are then suggested to adjust their opinions by using an adaptive individual opinion adjustment mechanism. Finally, detailed simulation results are provided to demonstrate the convergence of the proposed model and analyze different parameters’ effects on the stabilized time steps and the number of stable state opinion clusters.     

邓巴数限制下的社交网络回音室数量研究

大量研究发现，社交网络结构对观点的传播动力学特性有显著影响。提出的基于邓巴数限制的网络生成算法--DNCSN（Social Network Generation Algorithm with the Constraint of Dunbar’s Number），具有较小的平均距离和较大的群聚系数，可以生成与真实网络节点度和节点强度的非线性关系较为接近的结果。基于DNCSN生成算法，可以更为有效地研究社交网络上的观点演化特性。通过调整社交网络中个体的观点“坚定度”和观点取信阈值参数，对比研究了回音室的形成数量变化。研究表明当网络的观点初值均匀分布，网络规模小于邓巴数字时，相比于同等规模的全联通网络，DNCSN网络结构能在节点取信阈值大于0.15时减少网络的回音室的形成数量；当网络规模增大时，DNCSN网络的回音室形成数量多于全联通网络；当取信阈值小于0.1时，全联通网络的回音室形成数量受网络规模影响，且网络规模越小回音室数量越少。     

社会网络环境下基于公众行为大数据属性挖掘的大群体应急决策方法及应用

针对社会网络环境下复杂大群体应急决策中决策属性信息难以获得问题,提出社会网络环境下公众行为大数据驱动的大群体应急决策方法.首先,通过挖掘社交平台上的公众行为大数据,利用TF-IDF、Word2vec技术进行关键词提取、聚类及其影响力分析,从大量行为数据中挖掘大群体决策属性信息以辅助专家决策,使决策结果具有更高的科学性和有效性;其次,构建决策者间基于信任关系和观点相似度的社会网络,采用同时考虑信任和相似度的聚类方法对决策者进行聚类,并基于社会网络分析获得决策者权重;然后,提出基于决策者间信任关系的共识调整方法进行共识调整以获得最终群体决策矩阵和方案排序,通过引入决策者客观自信度避免个别决策者过分自信行为的影响;最后,通过一个新冠疫情案例分析说明方法的可行性和有效性.     

Fuzzy multi attribute group decision making method to achieve consensus under the consideration of degrees of confidence of experts’ opinions

The aim of this paper is to introduce a fuzzy multi attribute group decision making technique considering the degrees of confidence of experts’ opinions. In the process of decision making, each expert provides his/her evaluation over the alternatives depending on a finite set of attributes and constructs an individual fuzzy decision matrix. The proposed technique establishes an iterative process to aggregate the fuzzy information, given by individual expert, into group consensus opinion by using the fuzzy similarity measure. Then, based on group consensus opinion, the proposed approach utilizes the fuzzy similarity measure to find out the most desirable alternative through approximate reasoning. The proposed decision making technique is more flexible due to the fact that it considers the degrees of confidence of experts’ opinions. Finally an example has been shown to highlight the proposed methodology.     

社交网络中基于K核分解的意见领袖识别算法

针对在社交网络中挖掘意见领袖时存在的计算复杂度高的难题,提出了一种基于K核分解的意见领袖识别算法CR.首先,基于K核分解方法获取社交网络中的意见领袖候选集,以缩小识别意见领袖的数据规模;然后,提出包括位置相似性和邻居相似性的用户相似性的概念,利用K核值、入度数、平均K核变化率和用户追随者个数计算用户相似性,并根据用户相...     

An influence-driven feedback system for preference similarity network clustering based consensus group decision making model

Consensus group decision making (CGDM) allows the integration within this area of study of other advanced frameworks such as Social Network Analysis (SNA), Social Influence Network (SIN), clustering and trust-based concepts, among others. These complementary frameworks help to bridge the gap between their corresponding theories in such a way that important elements are not overlooked and are appropriately taken into consideration. In this paper, a new influence-driven feedback mechanism procedure is introduced for a preference similarity network clustering based consensus reaching process. The proposed influence-driven feedback mechanism aims at identifying the network influencer for the generation of advices. This procedure ensures that valuable recommendations are coming from the expert with most similar preferences with the other experts in the group. This is achieved by adapting, from the SIN theory into the CGDM context, an eigenvector-like measure of centrality for the purpose of: (i) measuring the influence score of experts, and (ii) determining the network influencer. Based on the initial evaluations on a set of alternatives provide by the experts in a group, the proposed influence score measure, which is named the σ-centrality, is used to define the similarity social influence network (SSIN) matrix. The σ-centrality is obtained by taking into account both the endogenous (internal network connections) and exogenous (external) factors, which means that SSIN connections as well as the opinion contribution from third parties are permitted in the nomination of the network influencer. The influence-driven feedback mechanism process is designed based on the satisfying of two important conditions to ensure that (1) the revised consensus degree is above the consensus threshold and that (2) the clustering solution is improved.     

Enhancing the flow experience of consumers in China through interpersonal interaction in social commerce

Although research on flow experience has recently received much attention, few studies have been published on the perceived interpersonal interaction factors of consumers and their influence in social commerce. In addition, few studies have focused on the impact of interpersonal interaction factors on flow experience. Drawing on the stimulus-organism-response framework, this study examines the impact of interpersonal interaction factors (perceived expertise, similarity, and familiarity) on the formation of flow experience and its subsequent effects on purchase intention in the context of social commerce. We investigate whether the impact of the three interpersonal interaction factors on flow experience differs between young and old users. We conduct a survey and collect 349 responses from users of a social shopping site in China. Our results indicate that interpersonal interaction factors positively relate to flow experience and subsequently influence purchase intention. We also find differences between young and old users in this area.