网络信息传播动力学建模研究

对网络传播机理建模是利用或控制网络传播的基础。网络信息传播系统具有复杂的动力学特征,其中的个体行为具有适应性。采用社会物理学研究思路,借鉴物理学振动传播的概念,对网络传播系统中各动力学因子进行了类比定义。根据动力学因子对网民传播行为的影响机理,建立具有随机性和适应性的网民信息传播微观动力学模型,使微观个体行为与宏观的系统状态关联起来。在此基础上构建仿真模型,通过仿真实验分析了网络传播系统S型函数的非线性演化特征,为类似系统的建模提供了新思路。

Abstract：

Modeling the mechanism of information diffusion is the basis of network diffusion utilizing and controlling. The diffusion of information on the Internet is with dynamical complexity, and the individual behavior is adaptive. Adopting the thinking in social physics, the dynamic factors in the information diffusion system on the analog of the spread of vibration in physics was defined. Then the microscopic dynamic model of net user’s randomicity and adaptability behavior was quantitatively built based on the mechanism of dynamic factor taking effect on the behavior of net users. In this way, the microscopic behavior was associated with system evolution at macro level. Simulation software was developed based on those models. The S_shape nonlinear evolution characteristics of information diffusion on the Internet with computing experiments were analyzed. This solution may be used in modeling similar systems.     

Three aspects on solving queuing service system in Shanghai world expo

This paper analyzes the queuing problem that was experienced during the Shanghai World Expo of2010.The queuing problem will be discussed at three levels.The three levels include the physical,psychological,and social aspects of the queuing problem.The collective behavior of visitors is studied through analyzing the information from the different sources,such as,the network of electronic eyes,the evaluation platform based on mobile phone system and the Internet.A hybrid information network technology is implemented.Based on the Wuli-Shili-Renli(WSR) system methodology,a new theoretical analysis framework for the queuing problem is proposed.     

Image segmentation algorithm based on high-dimension fuzzy character and restrained clustering network

An image segmentation algorithm of the restrained fuzzy Kohonen clustering network （RFKCN） based on high- dimension fuzzy character is proposed. The algorithm includes two steps. The first step is the fuzzification of pixels in which two redundant images are built by fuzzy mean value and fuzzy median value. The second step is to construct a three-dimensional （3-D） feature vector of redundant images and their original images and cluster the feature vector through RFKCN, to realize image seg- mentation. The proposed algorithm fully takes into account not only gray distribution information of pixels, but also relevant information and fuzzy information among neighboring pixels in constructing 3- D character space. Based on the combination of competitiveness, redundancy and complementary of the information, the proposed algorithm improves the accuracy of clustering. Theoretical anal- yses and experimental results demonstrate that the proposed algorithm has a good segmentation performance.     

A BI-LEVEL FORMULATION AND QUASI-NEWTON ALGORITHM FOR STOCHASTIC EQUILIBRIUM NETWORK DESIGN PROBLEM WITH ELASTIC DEMAND

1 IntroductionThe network design problem (NDP) is to select link improvements or add new links to anexisting network suCh that social welfare is maximized while according for the route choicebehavior of network users and/or changes in demand. Two sets of decision-makers with differedsobjectives are involved in the NDP. The network users individually select their routes suchthat their individual travel costs are minimized, while the planners aim to make the bestnetwork improvements for reduc…     

Iterative learning based fault diagnosis for discrete linear uncertain systems

In order to detect and estimate faults in discrete lin-ear time-varying uncertain systems, the discrete iterative learning strategy is applied in fault diagnosis, and a novel fault detection and estimation algorithm is proposed. And the threshold limited technology is adopted in the proposed algorithm. Within the chosen optimal time region, residual signals are used in the proposed algorithm to correct the introduced virtual faults with iterative learning rules, making the virtual faults close to these occurred in practical systems. And the same method is repeated in the rest optimal time regions, thereby reaching the aim of fault diagnosis. The proposed algorithm not only completes fault detection and estimation for discrete linear time-varying uncertain systems, but also improves the reliability of fault detection and decreases the false alarm rate. The final simulation results verify the validity of the proposed algorithm.     

Self-organizing fuzzy clustering neural network and application to electronic countermeasures effectiveness evaluation

A self-organizing fuzzy clustering neural network by combining the self-organizing Kohonen clustering network with the fuzzy theory is proposed. This network model is designed for the effectiveness evaluation of electronic countermeasures, which not only exerts the advantages of the fuzzy theory, but also has a good ability in machine learning and data analysis. The subjective value of sample versus class is computed by the fuzzy computing theory, and the classified results obtained by self-organizing learning of Kohonen neural network are represented on output layer. Meanwhile, the fuzzy competition learning algorithm keeps the similar information between samples and overcomes the disadvantages of neural network which has fewer samples. The simulation result indicates that the proposed algorithm is feasible and effective.     

Linguistic Valued Association Rules

1　IntroductionData mining is the process of discovering interesting knowledge from large amounts of datastored either in database,data warehouses,or information repositories. The mining ofassociation rules is one of the most important topics in the field of data mining. Variousmining algorithms about association rules have been discussed in many literatures such as[1]and[2 ].The linguistic valued association rules are discussed in this paper.In Section2 ,the FCM algorithm in [3]is adopted t…     

基于网络浏览行为的小众领域用户画像建模

基于网络浏览行为,研究小众领域的用户画像建模方法.本文提出构造领域文本伪本体的方法,并从用户的网络浏览行为中挖掘用户兴趣,生成了基于领域兴趣的用户画像,随后将构建的用户画像应用于个性化推荐领域,解决了小众领域因用户量少、信息不足而难以精准刻画用户画像的问题.该方法在以下三方面显著不同于其他相关研究工作:1)基于领域文本快速构建领域伪本体,构建基于伪本体的用户画像建模方法;2)采用词向量将网页映射到伪本体,构建画像生成算法;3)基于领域概念间相似度构建画像优化算法.最后,本文使用了交响乐团的售票数据及用户的网络浏览数据,采用多个指标进行实证分析,验证了本文提出的画像建模方法的有效性与合理性.     

基于多维学习的服务扩散模型构建与实证

基于主体认知渠道和多维消费者网络结构的线下线上异构性,构建多阶段时间序列学习（MSTSL）模型和多维网络学习（MNL）模型,探索多维网络融合的服务扩散规律;基于真实的扩散数据,改进消费者网络的服务扩散模型,并克服社会网络分析的仿真方法缺陷. 以图书服务数据样本进行实证研究发现,本文提出服务扩散模型能够较好地拟合现实数据;服务扩散中消费者的多维学习效应可以发挥重要作用;揭示消费者群体服务采纳服务产品的不同学习影响机制,研究结果有益于深入描述服务产品扩散的内在规律及制定恰当的服务策略.     

基于社会学习的创新扩散模型构建与实证

研究异质消费者的社会学习效应和创新扩散模型对于创新企业具有重要意义. 根据社会学习的主体认知过程和消费者网络结构的线上线下差异性, 构建两阶段社会学习（TSSL）模型和线上线下（O2O）学习模型, 探索多层次网络叠加条件下的扩散规律, 弥补传统扩散模型和社会网络分析的仿真方法缺陷. 针对我国图书发行扩散数据进行实证研究发现, 基于消费者网络的创新扩散模型较好地拟合样本数据; 在图书发行扩散中消费者的社会学习效应发挥重要作用; 基于消费者网络的创新扩散模型表明, 各图书产品面对消费者网络拓扑结构的非一致性, 揭示各产品消费者群体创新采纳的学习影响机制差异性, 研究结果有益于深入了解创新产品扩散的内部规律及制定针对性的营销策略.     

能源区块链网络中的虚拟电厂运行与调度模式

为提高虚拟电厂中用户交易的效率及其运行调度的安全性，针对传统虚拟电厂运行调度中高成本、高风险、低效率的问题，基于多元用电主体概念，提出了能源区块链网络中的虚拟电厂运行与调度模式。为提高用户交易的可靠性和经济性，采用电力交易共识算法对虚拟电厂中不同需求的多元用户进行智能匹配；引入能源区块链网络，提出改进的虚拟电厂运行调度模型，不仅保证了信息存储的安全性，还使得运行调度更加稳定透明。案例分析表明，本文提出的模型能够有效提高虚拟电厂中交易与调度的安全性和效率，可以为虚拟电厂中的能源交易提供参考。     

社会网络与创新扩散的共生演化

在基于协调博弈和动态网络的基础上,构建了一个社会网络与创新扩散的共生演化模型,采用仿真与计量相结合的方法,分析了创新扩散、网络整体收益和社会网络之间的相互影响。研究表明,个体行为与社会网络结构对创新扩散的影响,取决于创新自身相对优势的大小,具有相对优势的创新在密集的网络中和规则连接方式下更容易扩散。网络整体收益不仅与创新自身以及采用个体的自身特征有关,也与局部网络效应强弱和个体之间建立联系的成本相关。创新扩散微观层面中个体策略选择行为特征会导致社会网络结构特征(如网络异质性、小世界效应,度相关性)的变化。     

小世界社会网络中的信息传播

小世界社会网络模型是一种描述现实社会系统的理想模型,基于此模型,提出小世界社会网络中的信息传播模型。模型中,每个社会参与者都具有一个连续型参数来描述其对于信息的态度,正的态度对信息传播有正反馈作用,而负的态度对于信息传播有负反馈作用。同时,引入局部信息和全局信息概念用以描述社会参与者对于信息的获取能力。信息传播模型在不同随机度和规模的小世界社会网络上进行了计算仿真。仿真结果表明网络的拓扑结构和参与者态度的正负反馈作用以及对于全局信息的权重对于信息的传播均衡结果有显著的影响。     

社会网络下更新产品扩散的投放时机和种子优化

近年来，更新产品的延期投放成为许多企业的产品运营策略。目前关于产品投放时机的研究主要基于Bass模型，假定同质用户，没有考虑具体的社会网络，也没有优化初始的投放种子选择。基于社会影响力最大化模型的更新产品投放时机和种子优化问题：在一个已存在旧产品的社会网络 中，产品以扩散模型 的形式传播其影响力，但更新产品投放时旧产品停止扩散，如何选择投放阶段 和 个更新产品的种子使得新旧产品利润之和最大化。在本文的扩散模型中，如果用户来自已购买产品的邻居影响力超过某一确定阈值，则该用户保持激活并影响其未激活邻居，当未有新的激活用户时停止扩散。本文建立了更新产品扩散的投放时机和种子优化问题的整数规划模型，设计了求解大规模问题的多阶段贪婪算法。计算实验显示，相比传统贪婪算法，该算法具有更高的求解效率，相比度数下降算法、随机算法，该算法具有更好的求解质量。同时在一个真实网络中进行扩散模拟，发现：更新产品利润小、种子数量少、计划阶段限制大时，延期投放容易使厂商获得更高的利润和市场效果。     

基于数据爬虫与动态社会网络的公众环保行为演化研究

本文运用数据爬虫技术与社会网络分析方法对一个豆瓣公众环境保护小组的信息交互及相应的动态社会网络演化过程进行刻画,并在基于计划行为理论的绿色出行行为意愿模型的基础上:对嵌入在动态社会网络中的公众环保行为演化规律进行了研究.结果表明:在公众环境保护小组的社会网络形成过程中,社会网络的拓扑结构由最初的小世界网络特征迅速演变成为无标度网络特征;降低社会网络中行为主体的信息交互阈值、公众环保信息的差异程度、行为主体的易受影响程度将有利于公众环保豆瓣小组社会网络中信息的有效传播,行为主体对于公众环保信息的掌握与认知将能演化达到更加理想的状态,也将有利于促进公众环保行为的推广.