Topic modeling with network regularization

Qiaozhu Mei, Deng Cai, Duo Zhang, Cheng Xiang Zhai

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


In this paper, we formally define the problem of topic modeling with network structure (TMN). We propose a novel solution to this problem, which regularizes a statistical topic model with a harmonic regularizer based on a graph structure in the data. The proposed method combines topic modeling and social network analysis, and leverages the power of both statistical topic models and discrete regularization. The output of this model can summarize well topics in text, map a topic onto the network, and discover topical communities. With appropriate instantiations of the topic model and the graph-based regularizer, our model can be applied to a wide range of text mining problems such as author-topic analysis, community discovery, and spatial text mining. Empirical experiments on two data sets with different genres show that our approach is effective and outperforms both text-oriented methods and network-oriented methods alone. The proposed model is general; it can be applied to any text collections with a mixture of topics and an associated network structure.

Original languageEnglish (US)
Title of host publicationProceeding of the 17th International Conference on World Wide Web 2008, WWW'08
Number of pages10
StatePublished - 2008
Event17th International Conference on World Wide Web 2008, WWW'08 - Beijing, China
Duration: Apr 21 2008Apr 25 2008

Publication series

NameProceeding of the 17th International Conference on World Wide Web 2008, WWW'08


Other17th International Conference on World Wide Web 2008, WWW'08


  • Graph-based regularization
  • Social networks
  • Statistical topic models

ASJC Scopus subject areas

  • Computer Networks and Communications


Dive into the research topics of 'Topic modeling with network regularization'. Together they form a unique fingerprint.

Cite this