文章基本信息

标题：A Dynamic Model of Reposting Information Propagation Based on Empirical Analysis and Markov Process
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作者：Gui-Xun Luo ; Yun Liu ; Zhi-Yuan Zhang 等
期刊名称：Journal of Universal Computer Science
印刷版ISSN：0948-6968
出版年度：2016
卷号：22
期号：3
页码：360-374
DOI：10.3217/jucs-022-03-0360
出版社：Graz University of Technology and Know-Center
摘要：In this paper, based on abundant data from Sina Weibo, we perform a comprehensive and in-depth empirical analysis of repostings and draw some conclusions. First, in regards to quantity, reposting takes up a large proportion of daily microblog activity. Second, the depth of repostings follows an exponential distribution and the first three orders of repostings hold 99 percent of the total amount of reposting, which provides an important foundation for solving the question of Influence Maximization. Third, the time interval for repostings also obeys exponential distribution. Therefore, we have built a dynamic information propagation model in terms of conclusions drawn from Weibo data and the Continuous-Time Markov Process. Due to the basis of the temporal network, our proposed model can change with the time and structure of a network, thus giving it good adaptability and predictability as compared to the traditional information diffusion model. From the final simulation results, our proposed model achieves a good predictive effect.
关键词：continuous-time Markov process; information propagation model; reposting