计算机应用 ›› 2015, Vol. 35 ›› Issue (2): 322-325.DOI: 10.11772/j.issn.1001-9081.2015.02.0322

• 网络与通信 • 上一篇    下一篇

基于局域信息的社交网络信息传播模型

程晓涛, 刘彩霞, 刘树新   

  1. 国家数字交换系统工程技术研究中心, 郑州 450002
  • 收稿日期:2014-09-15 修回日期:2014-11-12 出版日期:2015-02-10 发布日期:2015-02-12
  • 通讯作者: 程晓涛
  • 作者简介:程晓涛(1990-),男,河北邢台人,硕士研究生,主要研究方向:复杂网络、数据挖掘; 刘彩霞(1974-),女,山东烟台人,副教授,博士,主要研究方向:移动通信网安全; 刘树新(1987-),男,山东潍坊人,博士研究生,主要研究方向:复杂网络、移动通信网安全。
  • 基金资助:

    国家科技重大专项(2013ZX03006002)。

Information propagation model for social network based on local information

CHENG Xiaotao, LIU Caixia, LIU Shuxin   

  1. China National Digital Switching System Engineering and Technological R&D Center, Zhengzhou Henan 450002, China
  • Received:2014-09-15 Revised:2014-11-12 Online:2015-02-10 Published:2015-02-12

摘要:

针对传统传播模型更适用于均匀网络而无法有效应用于现实非均匀无标度社交网络的问题,提出一种基于用户局域信息的社交网络信息传播模型。模型中考虑了无标度网络中用户间拓扑特征差异和用户影响力不同对信息传播的影响,根据节点周边邻居节点的感染情况和权威性计算感染概率,模拟现实社交网络中的信息传播情况。通过在采集的真实微博网络数据上进行仿真实验,结果表明该模型较传统的SIR模型更能体现社交网络中信息传播的快速性与范围的广泛性;同时,通过调整模型中的相关参数,验证了相关管控措施对传播效果的影响。

关键词: 社交网络, 信息传播, 复杂网络, 传染病模型, 用户影响力

Abstract:

The traditional information propagation model is more suitable for homogeneous network, and cannot be effectively applied to the non-homogeneous scale-free Social Network (SN). To solve this problem, an information propagation model based on local information was proposed. Topological characteristic difference between users and different effect on information propagation of user influence were considered in the model, and the probability of infection was calculated according to the neighbor nodes' infection and authority. Thus it could simulate the information propagation on real social network. By taking simulation experiments on Sina microblog networks, it shows that the proposed model can reflect the propagation scope and rapidity better than the traditional Susceptible-Infective-Recovered (SIR) model. By adjusting the parameters of the proposed model, it can verify the impact of control measures to the propagation results.

Key words: Social Network (SN), information propagation, complex network, epidemic model, user influence

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