Multi-Topic Tracking Model for dynamic social network
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- 作者：Yuhua Lia ; idcliyuhua@hust.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Changzheng Liua ; liucz@sina.cn" class="auth_mail" title="E-mail the corresponding author ; Ming Zhaob ; mingzhao@asu.edu" class="auth_mail" title="E-mail the corresponding author ; Ruixuan Lia ; rxli@hust.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Hailing Xiaoa ; harryshayne@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Kai Wanga ; wk0719@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Jun Zhanga ; zhangjun9204@gmail.com" class="auth_mail" title="E-mail the corresponding author
- 关键词：Multi-Topic Tracking Model ; Dynamic social network ; Influence phenomenon ; Selection phenomenon
- 刊名：Physica A: Statistical Mechanics and its Applications
- 出版年：2016
- 出版时间：15 July 2016
- 年：2016
- 卷：454
- 期：Complete
- 页码：51-65
- 全文大小：496 K
文摘
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The selection behavior of users is considered in our topic tracking model.
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Selection model is combined with influence model to model dynamics of social links.
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A novel statistical model MTTD is proposed.
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MTTD can track multi-topic of dynamic social networks simultaneously.