中文摘要：

在社会网络话题模型中,一些频繁出现的单词往往出现在不同的话题中。用户对这些单词感兴趣,因而分析时不能省略,这给话题分析带来了严重的挑战。为了解决这一问题,对话题模型中的节点流行性进行建模,提出了一种考虑节点重要性的LDA（latent Dirichlet allocation）社会网络话题模型。在该模型中,提出了流行性组件的概念,并提出了一种包含了流行性组件的扩展话题模型。通过实验结果表明,提出的包含流行性组件的扩展话题模型具有更好的预测能力,其预测结果的准确性明显优于现有的相关研究。

英文摘要：

In topic models of the social networks,these are usually some frequent words,such as Barack Obama,and they appear in many different topics. Users usually are interested in these words,so they cannot be removed while analyzing,and this poses a huge challenge for topic analysis. In order to solve this problem,this paper took popularity into consideration while modeling topics. This paper defined the concept of popularity for nodes in the social networks,and proposed an extended topic model containing node popularity. The experiments show that,the proposed model has better prediction ability,and is more effective in prediction accuracy than related works.