| 博客网络中具有突发性的话题传播模型 |
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| 引用本文： | 赵丽,袁睿翕,管晓宏,贾庆山.博客网络中具有突发性的话题传播模型[J].软件学报,2009,20(5):1384-1392. |
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| 作者姓名： | 赵丽 袁睿翕 管晓宏 贾庆山 |
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| 作者单位： | 1. 清华大学自动化系智能与网络化系统研究中心,北京,100084
2. 清华大学自动化系智能与网络化系统研究中心,北京,100084;西安交通大学,智能网络与网络安全教育部重点实验室,陕西,西安,710049 |
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| 基金项目： | Supported by the National Natural Science Foundation of China under Grant Nos.60574087, 60736027, 60704008 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant Nos.2007AA01Z480, 2007AA01Z475, 2007AA01Z464 (国家高技术研究发展计划(863)); the Program of Introducing Talents of Discipline to Universities of China under Grant No.B06002 (高等学校学科创新引智计划); the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20070003110 (高等学校博士学科点专项科研基金) |
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| 摘 要： | 提出了一个基于节点知名度和活跃度的离散时间话题传播模型.该模型的参数具有明确的物理意义,可体现话题动态传播过程的特征,并可为话题传播趋势的预测研究提供依据.通过统计和分析中国最大的博客站 点——新浪博客在几个月中若干具有突发性的事件引起的热门话题数据,结果表明,所提出的模型可以较为精确地再现话题的实际传播过程并体现传播速率的重尾现象.
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| 关 键 词： | 博客网络 节点知名度 节点活跃度 话题场强 话题传播 |
| 收稿时间： | 2008/3/10 0:00:00 |
| 修稿时间： | 2008/10/27 0:00:00 |
Bursty Propagation Model for Incidental Events in Blog Networks |
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| ZHAO Li,YUAN Rui-Xi,GUAN Xiao-Hong and JIA Qing-Shan.Bursty Propagation Model for Incidental Events in Blog Networks[J].Journal of Software,2009,20(5):1384-1392. |
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| Authors: | ZHAO Li YUAN Rui-Xi GUAN Xiao-Hong and JIA Qing-Shan |
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| Affiliation: | Center for Intelligent and Networked Systems;Department of Automation;Tsinghua University;Beijing 100084;China;Ministry of Education Key Laboratory for Intelligent Networks and Network Security;Xi'an Jiaotong University;Xi'an 710049;China |
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| Abstract: | A discrete time dynamic model is proposed for bursty propagation of incidental events based on the node popularity and activeness in blog networks. The parameters of this model are clearly associated with the actual propagation and can reflect the characteristics of the dynamic propagation process. The model can provide a basis for predicting the trend of social events propagation in blog networks. Numerical testing is performed with the data from widely discussed events in Sina Blog, one of the most popular blogospheres in China in several months, and the results show that this model can emulate the actual event propagation and reflect the heavy tail phenomena of the decreasing propagation rate. |
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| Keywords: | blog network node popularity node activeness topic field strength topic propagation |
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