Studying the co-evolution of information diffusion,vaccination behavior and disease transmission in multilayer networks with local and global effects

Today,with the rapid development of the internet,a large amount of information often accompanies the rapid transmission of disease outbreaks,and increasing numbers of scholars are studying the relationship between information and the disease transmission process using complex networks.In fact,the disease transmission process is very complex.Besides this information,there will often be individual behavioral measures and other factors to consider.Most of the previous research has aimed to establish a two-layer network model to consider the impact of information on the transmission process of disease,rarely divided into information and behavior,respectively.To carry out a more in-depth analysis of the disease transmission process and the intrinsic influencing mechanism,this paper divides information and behavior into two layers and proposes the establishment of a complex network to study the dynamic co-evolution of information diffusion,vaccination behavior,and disease transmission.This is achieved by considering four influential relationships between adjacent layers in multilayer networks.In the information layer,the diffusion process of negative information is described,and the feedback effects of local and global vaccination are considered.In the behavioral layer,an individual's vaccination behavior is described,and the probability of an individual receiving a vaccination is influenced by two factors:the influence of negative information,and the influence of local and global disease severity.In the disease layer,individual susceptibility is considered to be influenced by vaccination behavior.The state transition equations are derived using the micro Markov chain approach(MMCA),and disease prevalence thresholds are obtained.It is demonstrated through simulation experiments that the negative information diffusion is less influenced by local vaccination behavior,and is mainly influenced by global vaccination behavior;vaccination behavior is mainly influenced by local disease conditions,and is less influenced by global disease conditions;the disease transmission threshold increases with the increasing vaccination rate;and the scale of disease transmission increases with the increasing negative information diffusion rate and decreases with the increasing vaccination rate.Finally,it is found that when individual vaccination behavior considers both the influence of negative information and disease,it can increase the disease transmission threshold and reduce the scale of disease transmission.Therefore,we should resist the diffusion of negative information,increase vaccination proportions,and take appropriate protective measures in time.

A comparative study of information diffusion in weblogs and microblogs based on social network analysis

Purpose: This paper intends to explore a quantitative method for investigating the characteristics of information diffusion through social media like weblogs and microblogs.By using the social network analysis methods,we attempt to analyze the different characteristics of information diffusion in weblogs and microblogs as well as the possible reasons of these differences.Design/methodology/approach: Using the social network analysis methods,this paper carries out an empirical study by taking the Chinese weblogs and microblogs in the field of Library and Information Science(LIS) as the research sample and employing measures such as network density,core/peripheral structure and centrality.Findings: Firstly,both bloggers and microbloggers maintain weak ties,and both of their social networks display a small-world effect. Secondly,compared with weblog users,microblog users are more interconnected,more equal and more capable of developing relationships with people outside their own social networks. Thirdly,the microblogging social network is more conducive to information diffusion than the blogging network,because of their differences in functions and the information flow mechanism. Finally,the communication mode emerged with microblogging,with the characteristics of micro-content,multi-channel information dissemination,dense and decentralized social network and content aggregation,will be one of the trends in the development of the information exchange platform in the future.Research limitations: The sample size needs to be increased so that samples are more representative. Errors may exist during the data collection. Moreover,the individual-level characteristics of the samples as well as the types of information exchanged need to be further studied.Practical implications: This preliminary study explores the characteristics of information diffusion in the network environment and verifies the feasibility of conducting a quantitative analysis of information diffusion through social media. In addition,it provides insight into the characteristics of information diffusion in weblogs and microblogs and the possible reasons of these differences.Originality/value: We have analyzed the characteristics of information diffusion in weblogs and microblogs by using the social network analysis methods. This research will be useful for a quantitative analysis of the underlying mechanisms of information flow through social media in the network environment.

Bayesian Network Model of Product Information Diffusion and Reasoning of Influence

Information diffusion on social media has become a key strategy in people’s daily interactions. This paper studies consumers’ participation in the product information diffusion, and analyzes the complexity of information diffusion which is affected by many factors. Prior investigations of information diffusion have primarily focused on the composition of diffusion networks with independent factors and the intricacy of the process has not been completely evaluated. The majority of prior investigations have focused on strategies and the moving forces in social media processes and the determination of influential seed nodes, with few evaluations conducted about the factors affecting consumers’ choices in information diffusion. In this study, a Bayesian network model of product information diffusion was created to examine the links between factors and consumer deportment. It revealed how those factors had an impact on each other and on consumer deportment choice. The innovation of the thesis is reflected in the exploration and analysis of the specific communication path of product information diffusion, which provides a better marketing idea and practical method for the development of mobile e-commerce. The research findings can help identify the quantitative relationships between the factors affecting the process of product information diffusion and user behavior.

Subtle role of latency for information diffusion in online social networks

Information diffusion in online social networks is induced by the event of forwarding information for users, and latency exists widely in user spreading behaviors. Little work has been done to reveal the effect of latency on the diffusion process. In this paper, we propose a propagation model in which nodes may suspend their spreading actions for a waiting period of stochastic length. These latent nodes may recover their activity again. Meanwhile, the mechanism of forwarding information is also introduced into the diffusion model. Mean-field analysis and numerical simulations indicate that our model has three nontrivial results. First, the spreading threshold does not correlate with latency in neither homogeneous nor heterogeneous networks, but depends on the spreading and refractory parameter. Furthermore, latency affects the diffusion process and changes the infection scale. A large or small latency parameter leads to a larger final diffusion extent, but the intrinsic dynamics is different. Large latency implies forwarding information rapidly, while small latency prevents nodes from dropping out of interactions. In addition, the betweenness is a better descriptor to identify influential nodes in the model with latency, compared with the coreness and degree. These results are helpful in understanding some collective phenomena of the diffusion process and taking measures to restrain a rumor in social networks.

A mixed stochastic user equilibrium model considering influence of advanced traveller information systems in degradable transport network

Advanced traveler information systems （ATIS） can not only improve drivers＇ accessibility to the more accurate route travel time information, but also can improve drivers＇ adaptability to the stochastic network capacity degradations. In this paper, a mixed stochastic user equilibrium model was proposed to describe the interactive route choice behaviors between ATIS equipped and unequipped drivers on a degradable transport network. In the proposed model the information accessibility of equipped drivers was reflected by lower degree of uncertainty in their stochastic equilibrium flow distributions, and their behavioral adaptability was captured by multiple equilibrium behaviors over the stochastic network state set. The mixed equilibrium model was formulated as a fixed point problem defined in the mixed route flows, and its solution was achieved by executing an iterative algorithm. Numerical experiments were provided to verify the properties of the mixed network equilibrium model and the efficiency of the iterative algorithm.

Evaluation of Microblog Users’ Influence Based on PageRank and Users Behavior Analysis

This paper explores the uses’ influences on microblog. At first, according to the social network theory, we present an analysis of information transmitting network structure based on the relationship of following and followed phenomenon of microblog users. Informed by the microblog user behavior analysis, the paper also addresses a model for calculating weights of users’ influence. It proposes a U-R model, using which we can evaluate users’ influence based on PageRank algorithms and analyzes user behaviors. In the U-R model, the effect of user behaviors is explored and PageRank is applied to evaluate the importance and the influence of every user in a microblog network by repeatedly iterating their own U-R value. The users’ influences in a microblog network can be ranked by the U-R value. Finally, the validity of U-R model is proved with a real-life numerical example.

Introduction to information seeking behavior—A review of literature and field practice directions

The article tries to discover the major authors in the field of information seeking behavior via social network analysis. It is to be accomplished through a literature review and also by focusing on a graphic map showing the seven most productive coauthors in this field. Based on these seven authors' work, five probable research directions about information seeking behavior are discerned and presented.

Collaborative Diffusion Model of Information and Behavior in Social Networks

Information diffusion may lead to behaviors related to information content.This paper considers the co-existence of information and behavior diffusion in social networks.The state of users is divided into six categories,and the rules and model of collaborative diffusion of information and behavior are established.The influence of different parameters and conditions on the proportions of behavior diffusion nodes and information diffusion ones is analyzed experimentally.The results show that the proportion of nodes taking action in uniform networks is higher than that in non-uniform networks.Although users are more likely to take actions related to information content after spreading or knowing information,the results show that it has little influence on the proportion of users taking action.The proportion is mainly affected by the probability that users who do not take action become ones who take.The greater the probability,the less the proportion of nodes who know information.In addition,compared with choosing the same node as the initial information and behavior diffusion node,choosing different nodes is more beneficial to the diffusion of behaviors related to information content.

基于CPSS视角的需求响应能力评估综述

对信息—物理—社会系统与需求响应评估的结合进行综述。首先对信息物理系统的历史和框架进行分析。接着总结了现有的需求响应研究现状,包括需求响应潜力评估的意义、分类和评估方法。对于需求响应能力评估的数据来源进行总结,包括问卷调查和运行数据采集。在信息—物理—社会系统与需求响应结合方面,分别分析需求响应的物理域、信息域和社会域基础,介绍相应的建模方法和研究内容。最后,对未来的研究展望进行讨论,包括市场考核机制、快速仿真与建模技术以及综合能源下的需求响应管理。

突发事件中网民信息异化模仿行为的类型及发生机理研究

[研究目的]分析突发事件中网民信息异化模仿行为类型及发生机理,为网络舆情回归健康状态提供支持。[研究方法]整合现有理论分析视域,从政府公权力和网民行为取向两维度构建突发事件中网民信息异化模仿行为的解释框架,提炼概括网民模仿行为类型,继而应用案例研究法对网民模仿行为进行分析,探寻突发事件中网民信息异化模仿行为的发生机理。[研究结论]突发事件中网民信息异化模仿行为可以分为集群式、散点式、观览式、终止式四种。突发事件或突发事件情景的同源性、同质性和可比性、信息异化模仿行为低成本与高收益、突发事件中信息传导行为内卷化均是模仿行为发生的重要原因。解决该问题,需遏制信息异化生产、分型处置模仿行为以及营造良好网络舆论环境。

网络结构视角下用户异质性对知识协作效果的影响研究

[目的/意义]探讨如何优化在线知识协作平台网络结构和平衡团队内部的知识与经验,为促进跨领域信息流通,避免信息孤岛的形成,通过集体知识协作,推动知识的创造、传播和应用。[方法/过程]本研究利用编程问答网站Stack Overflow的数据,筛选出含“Python”标签的问题帖和对应的回答帖,结合负二项回归模型探讨了网络结构特征和用户异质性对知识协作质量和效率的影响。[结果/结论]网络结构特征方面,节点中心性显著提升了协作的质量和效率,较高的聚集系数和较大的连边跨度限制了信息流通,不利于知识协作效率。用户异质性方面,知识背景和注册时长的高异质性通常阻碍协作,经验异质性中注册时长异质性对协作效果均产生负面影响,回答被采纳率异质性仅对协作质量产生了负面影响,而活动强度异质性则产生了积极影响。

疾病传播过程中的层间耦合影响问题研究

疾病传播受到信息传播、个体行为选择等因素的影响,过程复杂,为了研究各种因素对疾病传播影响的内在机理,提出了一个交互演化的层间耦合传播模型,分析信息传播、接种行为和疾病传播的动态过程所涉及的6种耦合影响关系。在耦合模型的信息层,讨论行为和疾病对信息传播的影响;在耦合模型的行为层,讨论信息和疾病对接种行为的影响;在耦合模型的疾病层,讨论信息和行为对疾病传播的影响。采用微观马尔科夫链方法分析该模型,得到耦合状态转化方程和疾病流行阈值,通过模拟仿真实验讨论该耦合因素对疾病传播阈值和疾病传播规模的影响。研究结果表明,加强信息的传播,鼓励个体接种疫苗,是更好地控制疾病传播的两大关键因素。

人际互动作用下集群行为的演化逻辑与治理

中国社会正经历全面转型阶段,社会利益主体的阶层分化和利益冲突使阶层关系日趋复杂,由此引发的集群行为对中国公共安全造成严重威胁。针对不同社会情景下集群行为演化逻辑进行科学分析,进而为有效控制和预防恶性集群事件(行为)提供参考,具有重要的社会意义。集群行为是公共管理经典研究问题,传统的社会科学研究范式难以从微观视角属性理解集群行为的涌现特征以及整体结构,进而难以对机制进行精确剖析。在已有集群行为理论与研究基础上,创新性地构建了“理性与非理性动因—影响与独立场域”的综合解释框架,结合信息接收与处理机制的探讨对集群行为的演化逻辑进行理论上的再构建,形成了全局完备、局部完备、全局不完备与局部不完备信息传播机制下的演化链条;进一步引入社会计算领域中的复杂网络理论与信息传播模型,对集群行为演化传递过程建模。在总体框架指导下,依据不同情景与传播机制下的分析框架与演化模型对演化过程进行数学分析,发现集群行为的演化存在自校正性,较大的社会影响以及有限的时间是阻止集群行为自校正的环境制约,导致系统不可预测性与路径依赖性。在结构化的人群中,发现稀疏网络有利于集群行为的消散,但无标度网络增强了系统中社会影响的自强化性。通过计算机模拟以及基于农民工实证数据的仿真实验,对集群行为的一般化与特殊化机制进行讨论,发现农民工的正式网络结构与无标度网络结构在集群行为的演化上呈现相似特征,处在正式网络关键位置的农民工对于集群行为的发动具有重大影响。

信息内容平台治理“洗稿”行为的路径及其优化研究——基于网络知识产权安全视角

[研究目的]“洗稿”行为是网络知识产权风险在信息内容平台的典型表现形式,探究“洗稿”行为的信息内容平台治理路径对构建网络知识产权安全整体环境具有重要意义。[研究方法]结合相关研究,构建了基于网络知识产权安全视角的信息内容平台治理“洗稿”行为的分析框架,通过对5个典型图文、视频及综合类信息内容平台的案例调研和比较分析,总结信息内容平台治理“洗稿”行为的具体策略和优化路径。[研究结论]信息内容平台治理“洗稿”行为涵盖了“制度—管理—技术”三种路径,作用于平台用户行为管理和信息内容安全治理两个方面,并通过治理效果的内部与外部评价反馈进行持续优化。信息内容平台治理“洗稿”行为的路径优化重点是完善基础性的信息内容标准或规范、健全信息内容审核与用户监管机制、建立全链条的版权保护服务平台。

信息扩散机制下交通出行行为与疾病传播共演化动力学研究

为探究交通出行网络中出行策略受疾病传播的影响,基于演化博弈和复杂网络理论,提出信息扩散机制下交通出行行为与疾病传播的共演化动力学模型,构建包含物理层和虚拟层的双层网络,表征信息扩散和疾病传播的双重影响,并采用SIS模型刻画疾病传播机制。研究结果表明:在信息扩散机制下,恰当的交通出行行为可有效控制群体感染规模,即使在疾病传播能力较强时,整个群体中的感染规模仍然可以维持在较低水平;信息扩散范围越大,出行行为选择对群体中疾病传播的影响越显著。

新浪微博网信息传播分析与预测

文中以新浪微博为研究对象,以分析新浪微博的信息转发与传播特征为研究目的,并对传播行为进行预测.在获取大量新浪微博在线数据的基础上,对各种可能影响用户转发行为的因素进行统计、分析,挖掘各种影响因素特征并进行建模.提出基于用户属性、社交关系和微博内容三类综合特征,使用机器学习的分类方法,对给定微博的用户转发行为进行预测.基于微博网关注关系拓扑,利用概率级联模型对给定微博的转发路径进行预测,为预测微博的影响范围提供依据.文中通过实验分析了新浪微博符合复杂网络特征、社交类特征对转发行为有重要影响,并验证了传播预测的有效性.

微博网络测量研究

随着移动通信和Web技术的不断突破,以微博为代表的在线社会网络在中国广泛发展起来,越来越多的人开始使用微博进行信息分发和舆论传播.为了了解中国微博网络中的拓扑结构特征和用户行为特征等内在信息,对国内最大的微博系统——新浪微博——开展了主动测量,并结合已有的在线社会网络测量结果,对新浪微博的网络拓扑和用户行为特征进行了分析和比较.主要发现包括:1)新浪微博网络具有小世界特性;2)新浪微博网络的入度分布属于幂次分布,而出度分布表现为某种分段幂率函数;3)与类似社会网络相比,新浪微博网络的出入度不具有相关性;4)新浪微博网络属于同配网络;5)新浪微博用户发博时间具有明显的日分布和周分布模式;6)新浪微博用户博文数目分布表现为威布尔分布;7)新浪微博用户博文的转发和评价行为具有很强的相关性,且博文转发概率要高于评价概率.这些测量研究和发现不仅有助于设计出符合中国微博网络结构特征的数学模型和计算模型,也是实现对微博舆论的监测、引导、控制等方面的重要依据和基础.

基于用户行为的微博网络信息扩散模型

利用新浪微博数据对用户行为进行分析,在此基础上构建了基于用户行为的微博网络信息扩散模型SIRUB,同时计算了模型中各用户阅读微博和转发微博的概率,在微博网络中的实验表明,只有同时考虑阅读和转发概率时模型才能较准确地预测用户的转发行为.SIRUB模型对用户转发行为预测的F-score最高为0.228,高于经典SIR模型和SICR模型,此外该模型对微博扩散范围的预测其误差的均值和标准差也均小于SIR模型和SICR模型.

基于行为预测的微博网络信息传播建模

研究微博网络中的信息传播及扩散机制在市场营销、舆情管控等方面具有重要意义。当前的传播模型大多忽视了用户间的个体差异。为解决这一问题,提取了影响转发行为的四类特征,利用机器学习中的逻辑回归模型分析预测个体转发行为,并在此基础上融入用户个体差异,建立了一种基于行为预测的信息传播模型。实验表明,该模型能较好地模拟真实网络中的信息传播过程。

微博网络中用户主题兴趣相关性及主题信息扩散研究

运用Twitter-LDA主题模型对新浪微博数据进行了主题分析,基于用户主题兴趣相关性的研究表明用户间的主题兴趣具有三度相关性,同一主题兴趣下三度以内粉丝的发文数随用户发文数增加而波动式增加,各度粉丝与用户主题兴趣的相似度随粉丝度数的增加而下降。通过分析比较不同主题类别微博的扩散差异,发现生活情感类的信息最受用户欢迎,不同主题类别微博被转发的概率存在显著差异,平均转发数相差可达10倍,微博信息扩散树中各类主题在微博信息扩散深度、扩散时间间隔和用户的扩散能力方面都表现出不同的特征。