计算物理 ›› 2016, Vol. 33 ›› Issue (2): 234-252.

• 研究论文 • 上一篇    

在线社交网络中群体互动行为的时间特征

李旭军1,2, 刘业政1, 姜元春1   

  1. 1. 合肥工业大学管理学院, 合肥 230009;
    2. 安徽经济管理学院, 合肥 230059
  • 收稿日期:2015-01-17 修回日期:2015-07-01 出版日期:2016-03-25 发布日期:2016-03-25
  • 作者简介:LI Xujun(1976-),male,Ph.D,associate professor,major in dynamics of human behavior and dissemination of information on complex networks,E-mail:lixujun1976@163.com
  • 基金资助:
    Supported by Major Program of National Natural Science Foundation of China (71490725),National Key Basic Research Program of China (2013CB329600),National Science Foundation of China(9154611,71371062) and Key Project of Natural Science of Anhui Province Education Department(KJ2015A348)

Time Characteristics of Group Interaction Behavior in Online Social Network

LI Xujun1,2, LIU Yezheng1, JIANG Yuanchun1   

  1. 1. School of Management, Hefei University of Technology, Hefei 230009, China;
    2. Economy and Management School of Anhui, Hefei 230059, China
  • Received:2015-01-17 Revised:2015-07-01 Online:2016-03-25 Published:2016-03-25
  • Supported by:
    Supported by Major Program of National Natural Science Foundation of China (71490725),National Key Basic Research Program of China (2013CB329600),National Science Foundation of China(9154611,71371062) and Key Project of Natural Science of Anhui Province Education Department(KJ2015A348)

摘要: 针对在线社交网络中群体互动行为,运用有偏扩散理论,构建有偏扩散模型,从任务的间隔时间τ与执行时间θ的关系视角,系统地阐述了群体互动活动中群体发帖行为与事件行为的时间间隔分布特征.针对群体用户发帖时τθ的特征,论证群体用户发帖时间间隔的幂律分布机理.针对事件时τ>>θ的特征,论证了事件时间间隔具有指数效应的幂律分布机理,实验结果与理论推导吻合.

关键词: 在线社交网络, 群体互动, 有偏扩散, 幂律分布, 指数效应

Abstract: For interaction behavior of users in online social networks,we build a biased diffusion model to explain time interval distribution characteristics of group posting and event by relating task interval time τ and task execution time θ. We demonstrate that, for τθ,time intervals follow power law distribution; and for τ>>θ,time intervals follow power law distribution with exponential effect. Experimental results validate the model.

Key words: online social networks, group interaction, biased diffusion, power-law distribution, exponential effect

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