Relational Topic Models for Document Networks
Relational Topic Models for Document Networks
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发表时间：
2009-04
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通讯作者：
Jonathan D. Chang;D. Blei
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作者：
Jonathan D. Chang;D. Blei
我们开发了关系主题模型(RTM)，一种文档及其之间的链接的模型。对于每一对文档，RTM将其链接建模为以其内容为条件的二进制随机变量。该模型可以用来总结文档网络，预测它们之间的链接，并预测其中的单词。我们基于变分方法推导了有效的推理和学习算法，并对RTM在大型科学摘要和Web文档网络上的预测性能进行了评估。
We develop the relational topic model (RTM), a model of documents and the links between them. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them. We derive efficient inference and learning algorithms based on variational methods and evaluate the predictive performance of the RTM for large networks of scientific abstracts and web documents.