The last decade has seen great progress in both dynamic network modeling and topic modeling. This paper draws upon both areas to create a Bayesian method that allows topic discovery to inform the latent network model and the network structure to facilitate topic identification. We apply this method to the 467 top political blogs of 2012. Our results find complex community structure within this set of blogs, where community membership depends strongly upon the set of topics in which the blogger is interested.

翻译：在过去十年中,动态网络建模和主题建模都取得了巨大进步。本文件利用这两个领域创建了巴伊西亚州发现主题的方法,为潜在网络模式和网络结构提供信息,以方便专题识别。 我们将这种方法应用于2012年的467个顶尖政治博客。 我们的结果发现,在这套博客中,社区成员在很大程度上依赖于博客感兴趣的一系列主题。