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计算机工程

• 移动互联与通信技术 • 上一篇    下一篇

一种动态的移动社交网络拓扑模型

田雪颖1a,1b ,刘衍珩1a,1b ,孙 鑫2,王亚洲1a,林佳佳1a   

  1. (1. 吉林大学a. 计算机科学与技术学院;b. 符号计算与知识工程教育部重点实验室,长春130012;2. 中国海洋大学信息科学与工程学院,山东青岛266100)
  • 收稿日期:2013-05-17 出版日期:2014-09-15 发布日期:2014-09-12
  • 作者简介:田雪颖(1990 - ),女,硕士研究生,主研方向:网络拓扑建模,网络安全;刘衍珩,教授、博士、博士生导师;孙 鑫,博士; 王亚洲,学士;林佳佳,硕士研究生。
  • 基金资助:
    国家自然科学基金资助项目(60973136,61073164);吉林省科技发展计划青年科研基金资助项目(201101033);吉林大学国家 级创新基金资助项目(2012A53143)

A Dynamic Mobile Social Network Topology Model

TIAN Xue-ying 1a,1b ,LIU Yan-heng 1a,1b ,SUN Xin 2,WANG Ya-zhou 1a ,LIN Jia-jia 1a   

  1. (1a. College of Computer Science and Technology;1b. Key Laboratory of Symbolic Computation and Knowledge Engineering,Ministry of Education,Jilin University,Changchun 130012,China;2. College of Information Science and Engineering,Ocean University of China,Qingdao 266100,China)
  • Received:2013-05-17 Online:2014-09-15 Published:2014-09-12

摘要: 针对移动社交网络的动态性、用户不同重要性和信息交互有向性,基于4 种初始网络提出能准确描述移动社交网络结构的拓扑模型。采用随机游走理论和改进的PageRank 算法,引入过渡概率使每两时步之间的网络拓扑结构相互联系。通过PageRank 算法得到节点的势,进而求出概率过渡矩阵,利用随机游走理论由上一时步边存在概率矩阵和概率过渡矩阵得到当前时步边存在概率矩阵,每一时步动态地增加一个节点并检验是否有离开的节点。仿真结果显示,该模型在4 种初始网络下得到的网络拓扑结构,入度、出度、势分布以及度- 势相关性均具有明显幂律特性,表明随机游走理论和改进的PageRank 算法能较准确描述移动社交网络,具有一定的实践意义。

关键词: 社交网络, 网络拓扑, 随机游走, PageRank 算法, 过渡概率, 仿真模型

Abstract: A topological model that can describe the mobile social network accurately is proposed based on four initial networks considering the dynamic of social network,the different importance of users and the direction of information interaction. Random walking theory and improved PageRank algorithm are adopted,and transition probability is introduced to associate the network topological structure between two time-steps. Firstly,PageRank algorithm is used to obtain the strength of the nodes in order to get the probability transition matrix. Then random walking theory is used to get the current time-step edge existence probability matrix based on the last time-step edge existence probability matrix and the probability transition matrix. During each time-step,a node is added and it is checked if there is any departure node. Finally,simulation model is used to simulate the four initial networks in in-degree,out-degree,strength distribution and the correlation between degree and strength. The results indicate that the four initial networks’ in-degree,out-degree,strength distribution and the correlation between degree and strength show obvious power-law character. It shows that the random walking theory and improved PageRank algorithm can describe the mobile social network better,which is of certain practical significance.

Key words: social network, network topology, random walking, PageRank algorithm, transition probability, simulation model

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