一种基于用户相对权重的在线社交网络信息传播模型

根据在线社交网络信息传播特点和目前社交网络传播模型研究中存在的问题, 本文定义了网络用户之间的相互影响力函数, 在此基础上提出了一种基于用户相对权重的社交网络信息传播模型, 并对网络中的传播路径及传播过程进行了分析, 讨论了不同路径的信息传播影响力.为验证模型的有效性, 将传统的SIR模型和本文模型在六类不同网络拓扑下进行了仿真实验.仿真结果表明, 两类模型在均匀网络中没有明显差异, 但在非均匀网络中本文模型更能体现真实网络特点, 实验同时验证了节点的地位影响着信息的传播, 并且发现英文社交平台Twitter和中文社交平台新浪微博在拓扑结构上具备一定相似性.     

基于在线社交网络的信息传播模型

本文构造了一个基于在线社交网络的信息传播模型.该模型考虑了节点度和传播机理的影响,结合复杂网络和传染病动力学理论,进一步建立了动力学演化方程组.该方程组刻画了不同类型节点随着时间的演化关系,反映了传播动力学过程受到网络拓扑结构和传播机理的影响.本文模拟了在线社交网络中的信息传播过程,并分析了不同类型节点在网络中的行为规律.仿真结果表明:由于在线社交网络的高度连通性,信息在网络中传播的门槛几乎为零;初始传播节点的度越大,信息越容易在网络中迅速传播;中心节点具有较大的社会影响力;具有不同度数的节点在网络中的变 关键词： 在线社交网络 信息传播 微分方程 传染病动力学     

社交网络中信息传播的稳定性研究

社交网络已成为当前最重要的信息传播媒体之一,因此有必要研究信息在社交网络上的传播规律.本文探索了包含遏制机制和遗忘机制的信息传播机理,提出了信息传播的模型,给出了信息传播的规则,建立了相应的平均场方程,计算了平衡点和基本再生数R_0,并从理论上证明了平衡点的渐进稳定性.仿真实验分析了遏制机制、遗忘机制等因素对信息传播过程的影响,并验证了所得结论的正确性.     

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.     

社交网络中热点话题深度挖掘方法及实验测试

互联网大数据时代,社交网络数据交互具有实时性、社会性、复杂性。多元架构数据信息中会存在热点话题数据。传统社交网络热点话题挖掘方法存在热点词条检索速度慢、话题词条层浅、断层数据无法挖掘等一系列问题,如何将社交网络中的热点话题数据进行挖掘,针对这一问题提出大数据信息词条特征比对提取方法,对社交网络中的大数据数据信息词条进行特征显化处理,采用饼图对比方式对特征化词条数据进行网络互交频率展现,采用多维数据获取法,解决社交网络热点话题挖掘中出现的数据阻滞现象,满足社交网络中热点话题深度挖掘的要求。通过仿真实验对提出方法进行效率、准确度、速度测试,实验结果表明,提出方法对社交网络中的热点话题挖掘快捷、高效、实用性强。     

基于移动社交网络的谣言传播动力学研究

本文在CSR传播模型的基础上提出基于移动社交网络的CSR的谣言传播模型. 改进了CSR模型的传播规则和传播动力学方程, 使得更符合移动SNS上用户的使用习惯. 在CSR模型中的接受概率数学模型基础上, 考虑个人接受阈值对接受概率的影响, 更符合人类接受谣言的心理学特点. 本文对该传播模型进行了理论分析. 并在仿真实验中, 利用多agent仿真平台对新模型和CSR模型以及SIR模型 在匀质网络和异质网络中的传播效果进行了对比研究, 从实验的结果来看, 新的谣言传播模型在匀质网络中传播范围更广, 传播速度更快. 新模型具有初值敏感性的特点. 关键词： 复杂网络 移动社交网络 谣言传播     

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.     

基于朴素贝叶斯算法的社交网络数据挖掘技术研究

近年来,随着互联网技术飞速发展与普及,大量社交网络平台迅速崛起。社交网络平台拉近了日常人际关系,提供了便捷的信息通讯交流通道。同时,针对社交网络平台数据挖掘的技术研究成为不可缺少的网络数据研究领域一部分。现有社交网络数据挖掘技术所采用的传统数据挖掘算法与数据分离模式,存在大数据多元特征条件下,数据挖掘准确度降低、挖掘分类逻辑混乱等现象。针对问题产生根源,提出基于朴素贝叶斯算法的社交网络数据挖掘技术研究。采用基于朴素贝叶斯算法设计的PCIE-FN社交网络数据挖掘平台进行全面化的深入性解决。通过实验证明,提出的基于朴素贝叶斯算法的社交网络数据挖掘技术研究,各项数据满足社交网络数据挖掘日常应用要求。     

在线社交网络中谣言的传播与抑制

根据真实在线社交网络中谣言的传播特点以及有疾病潜伏期的传染病模型,提出一个新的基于在线社交网络的谣言传播SEIR模型.首先建立基于SEIR模型的动力学演化方程组,然后给出一个高效的抑制谣言传播的免疫策略——重要熟人免疫策略.最后在真实在线社交网络Facebook的用户数据集上,结合SEIR模型与动力学演化方程组以及包含重要熟人免疫策略在内的多种免疫策略,对免疫前后谣言传播的演化过程进行计算机仿真.仿真结果表明SEIR模型符合真实在线社交网络的传播特性,且重要熟人免疫策略是解决在线社交网路中谣言抑制问题的最佳方案.     

社交网络中基于贝叶斯和半环代数模型的节点影响力计算机理

本文综合考虑网络结构及节点间的互动等关键因素, 提出了一种节点影响力分布式计算机理. 首先根据节点交互行为在时域上的自相似特性, 运用带折扣因子的贝叶斯模型计算节点间的直接影响力; 然后运用半环模型来分析节点间接影响力的聚合; 最后根据社交网络的小世界性质及传播门限, 综上计算出节点的综合影响力. 仿真结果表明, 本文给出的模型能有效抑制虚假粉丝导致的节点影响力波动, 消除了虚假粉丝的出现对节点影响力计算带来的干扰, 从中选择影响力高的若干节点作为传播源节点, 可以将信息传播到更多数目的节点, 促进了信息在社交网络中的传播. 关键词： 社交网络 影响力 贝叶斯 半环代数     

Social network based microblog user behavior analysis

The influence of microblog on information transmission is becoming more and more obvious. By characterizing the behavior of following and being followed as out-degree and in-degree respectively, a microblog social network was built in this paper. It was found to have short diameter of connected graph, short average path length and high average clustering coefficient. The distributions of out-degree, in-degree and total number of microblogs posted present power-law characters. The exponent of total number distribution of microblogs is negatively correlated with the degree of each user. With the increase of degree, the exponent decreases much slower. Based on empirical analysis, we proposed a social network based human dynamics model in this paper, and pointed out that inducing drive and spontaneous drive lead to the behavior of posting microblogs. The simulation results of our model match well with practical situation.     

Blockchain-based group authentication scheme for 6G communication network

The ultimate objective in this period is to turn present technology into intelligent global habitats that ease daily interactions. The emerging Industry 4.0 has provided exciting potential technologies that have sped up the transition from the Internet of Things (IoT) to the Internet of Everything using advancements in artificial intelligence (IoE). A secure data transmission method and much higher networking rates are required by the exponential increase in the development and implementation of various 4.0-related industries, even if a blockchain currently employs a fast network like 5G. This highlights how 6G is required to meet the requirements of real-time applications. This research suggests a group-based handover authentication strategy for 6G heterogeneous networks, taking into account the handover of group users in real-world scenarios, to increase the efficiency and guarantee the security of the authentication process. The system consists of four components: the user UE, the access points gNB and eNB, the servers AAA and AAA*, and the widely used blockchain. Additionally, it is split into the EPC (evolved packet core) and 6GC domains by various network topologies (6G core network). To access network services, the UE must first carry out initial authentication and key negotiation with the local server. The system then performs handover authentication as well as batch authentication for individual and group users in handover scenarios, depending on the user count. The suggested scheme accomplished global switching authentication without making the switching process more difficult, accomplished group switching authentication by utilizing blockchain and aggregated signature technologies, and decreased the one-by-one authentication time by Yan using the D–H key exchange idea to accomplish known randomness security. Through formal examination and additional security analysis of the suggested protocol using the AVISPA tool, the results demonstrate that the protocol is secure.     

Behavior patterns of online users and the effect on information filtering

Understanding the structure and evolution of web-based user-item bipartite networks is an important task since they play a fundamental role in online information filtering. In this paper, we focus on investigating the patterns of online users’ behavior and the effect on recommendation process. Empirical analysis on the e-commercial systems show that users’ taste preferences are heterogeneous in general but their interests for niche items are highly clustered. Additionally, recommendation processes are investigated on both the real networks and the reshuffled networks in which real users’ behavior patterns can be gradually destroyed. We find that the performance of personalized recommendation methods is strongly related to the real network structure. Detailed study on each item shows that most hot items are accurately recommended and their recommendation accuracy is robust to the reshuffling process. However, the accuracy for niche items is relatively low and drops significantly after removing users’ behavior patterns. Our work is also meaningful in practical sense since it reveals an effective direction to improve the accuracy and the robustness of the existing recommender systems.     

视觉测量仿真系统及其网络布局优化研究

为了便于在开展测量前对测量方案和各环节进行分析、评价,设计了完整的视觉测量仿真系统,该系统可以为测量任务提供摄影网络布局、精度分析、测量模拟等多项功能。由于多站位、多角度的网络布局结构是提高视觉测量精度和可靠性的重要手段,对仿真系统中网络优化的目标、原理和方法进行了研究,针对网络优化设计了遗传算法。通过在薄膜充气可展开天线面形测量中的应用,证明了该系统的可行性。     

A method of segment protection based on p-cycle for dynamic multicast with limited-range wavelength converter in WDM network

In the optical multicast network, node and link failures have very important influence on the network survivability which may lead to multiple destinations cannot receive data. Based on the wavelength layered-graph method, a method of efficiency-score based on heuristic algorithm of pre-configured cycle (p-Cycle) based-segment protection (ESHS) for dynamic multicast with limited-range wavelength conversion was presented in this paper. By finding the multicast tree segment protection, the total available p-Cycles are constructed for the multicast. Then we calculate each p-Cycle efficiency-score, the highest efficient-score p-Cycle is selected as the multicast route protection p-Cycle. The simulation results show that the ESHS can get higher performance than the existing ESHT algorithm, in terms of multicast request blocking probability and wavelength utilization.     

基于远场信息和卷积神经网络的波前重构方法

探测波前相位信息是实现自适应光学波前补偿的关键,使用卷积神经网络（CNN）代替波前传感器进行波前重构,系统简单易于实现,同时重构过程不依赖迭代运算,快速实时。为准确提取远场中的波前特征,CNN需要事先使用大量样本进行训练。研究中根据4～30阶大气湍流泽尼克像差系数与其远场强度的对应关系,仿真制作样本数据集,训练CNN从输入的一帧远场图像中预测出畸变波前的泽尼克像差系数,重构原始波前。验证结果表明,该方法能快速实时地还原出波前相位信息,重构波前较原始波前具有极高的波面吻合度和较小的残差剩余量,有望实现实际自适应光学系统中的闭环校正。     

A study of the spreading scheme for viral marketing based on a complex network model

Buzzword-based viral marketing, known also as digital word-of-mouth marketing, is a marketing mode attached to some carriers on the Internet, which can rapidly copy marketing information at a low cost. Viral marketing actually uses a pre-existing social network where, however, the scale of the pre-existing network is believed to be so large and so random, so that its theoretical analysis is intractable and unmanageable. There are very few reports in the literature on how to design a spreading scheme for viral marketing on real social networks according to the traditional marketing theory or the relatively new network marketing theory. Complex network theory provides a new model for the study of large-scale complex systems, using the latest developments of graph theory and computing techniques. From this perspective, the present paper extends the complex network theory and modeling into the research of general viral marketing and develops a specific spreading scheme for viral marking and an approach to design the scheme based on a real complex network on the QQ instant messaging system. This approach is shown to be rather universal and can be further extended to the design of various spreading schemes for viral marketing based on different instant messaging systems.     

一种改进的基于信息传播率的复杂网络影响力评估算法

评价网络中节点的信息传播影响力对于理解网络结构与网络功能具有重要意义.目前,许多基于最短路径的指标,如接近中心性、介数中心性以及半局部(SP)指标等相继用于评价节点传播影响力.最短路径表示节点间信息传播途径始终选择最优方式,然而实际上网络间的信息传播过程更类似于随机游走,信息的传播途径可以是节点间的任一可达路径,在集聚系数高的网络中,节点的局部高聚簇性有利于信息的有效扩散,若只考虑信息按最优传播方式即最短路径传播,则会低估节点信息传播的能力,从而降低节点影响力的排序精度.综合考虑节点与三步内邻居间的有效可达路径以及信息传播率,提出了一种SP指标的改进算法,即ASP算法.在多个经典的实际网络和人工网络上利用SIR模型对传播过程进行仿真,结果表明ASP指标与度指标、核数指标、接近中心性指标、介数中心性指标以及SP指标相比,可以更精确地对节点传播影响力进行排序.