ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (1): 206-215.doi: 10.7544/issn1000-1239.2016.20148120

• 网络技术 • 上一篇    下一篇

基于主题模型的通信网络建模与仿真

李全刚,刘峤,秦志光   

  1. (电子科技大学计算机科学与工程学院 成都 610054) (quangang.l@163.com)
  • 出版日期: 2016-01-01
  • 基金资助: 
    国家“八六三”高技术研究发展计划基金项目(2011AA010706);国家自然科学基金重点项目(61133016);中央高校基本科研业务费专项资金(ZYGX2012J067)

Modeling and Simulation of Communication Network Based on Topic Model

Li Quangang, Liu Qiao, Qin Zhiguang   

  1. (School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu 610054)
  • Online: 2016-01-01

摘要: 探知通信网络的形成和演化机制是复杂网络领域中一个重要的研究点.众多研究者也提出了许多关于探索通信网络形成及演化机制的方法.现有的网络模拟方法主要着眼于网络的宏观特征而忽视了微观特征,导致个体用户模式的信息丢失.既然通信网络是与使用者的行为紧密相关的,那么构建模型时单用户的模式也应当被考虑进来.通过对网络中每个节点标注一个隐含属性——活跃度,提出一种基于主题模型的通信网络高效模拟生成方法.在真实邮件网络数据集上的实验结果验证了提出的方法能够很好地模拟原网络的整体特征和个体用户的行为模式.此外,由于隐私策略和访问权限的限制,对于大多数研究者而言,短时间内采集大规模的真实通信网络数据是十分困难的.许多研究工作因缺乏实验数据而受到制约,应对这个问题,可以使用该算法借助少量已有的通信数据流来生成大规模的模拟数据.该算法具有线性时间复杂度并且可以方便地并行化处理.

关键词: 通信网络, 生成模型, 模拟, 复杂网络, 通信流

Abstract: Understanding how communication networks form and evolve is a crucial research issue in complex network analysis. Various methods are proposed to explore networks generation and evolution mechanism. However, the previous methods usually pay more attention to macroscopic characteristics rather than microscopic characteristics, which may lead to lose much information of individual patterns. Since communication network is associated closely with user behaviours, the model of communication network also takes into consideration the individual patterns. By implicitly labeling each network node with a latent attribute-activity level, we introduce an efficent approach for the simulation and modeling of communication network based on topic model. We illustrate our model on a real-world email network obtained from email logs. Experimental results show that the synthetic network preserves some of the global characteristics and individual behaviour patterns. Besides, due to privacy policy and restricted permissions, it is arduous to collect a real large-scale communication network dataset in a short time. Much research work is constrained by the absence of real large-scale datasets. To address this problem, we can use this model to generate a large-scale synthetic communication network by a small amount of captured communication stream. Moreover, it has linear runtime complexity and can be paralleled easily.

Key words: communication network, generative model, simulation, complex network, communication stream

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