iTopicModel: Information network-integrated topic modeling

Yizhou Sun, Jiawei Han, Jing Gao, Yintao Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 probability. Some important practical issues in real applications are also discussed, including how to decide the topic number and how to choose a good network structure. We apply the model on two real datasets, DBLP and Cora, and the experiments show that this model is more effective in comparison with the state-of-the-art topic modeling algorithms.

Original languageEnglish (US)
Title of host publicationICDM 2009 - The 9th IEEE International Conference on Data Mining
Number of pages10
StatePublished - 2009
Event9th IEEE International Conference on Data Mining, ICDM 2009 - Miami, FL, United States
Duration: Dec 6 2009Dec 9 2009

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other9th IEEE International Conference on Data Mining, ICDM 2009
Country/TerritoryUnited States
CityMiami, FL


  • Document networks
  • Markov Random Field
  • Topic model

ASJC Scopus subject areas

  • General Engineering


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