iTopicModel: Information Network-Integrated Topic Modeling

—Document networks, i.e., networks associated with text information, are becoming increasingly popular due to the ubiquity of Web documents, blogs, and various kinds of online data. In this paper, we propose a novel topic modeling framework for document networks, which builds a unified generative topic model that is able to consider both text and structure information for documents. A graphical model is proposed to describe the generative model. On the top layer of this graphical model, we define a novel multivariate Markov Random Field for topic distribution random variables for each document, to model the dependency relationships among documents over the network structure. On the bottom layer, we follow the traditional topic model to model the generation of text for each document. A joint distribution function for both the text and structure of the documents is thus provided. A solution to estimate this topic model is given, by maximizing the log-likelihood of the joint probabili...

• Type: Conference
• Year: 2009
• Where: ICDM
• Authors: Yizhou Sun, Jiawei Han, Jing Gao, Yintao Yu
• Keywords: Data Mining, Generative Topic Model, Graphical Model, ICDM 2009, Topic Model