The Author-Topic-Community model for author interest profiling and community discovery

Chunshan Li, Kwok Wai CHEUNG, Yunming Ye, Xiaofeng Zhang*, Dianhui Chu, Xin Li

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

33 Citations (Scopus)


In this paper, we propose a generative model named the author-topic-community (ATC) model for representing a corpus of linked documents. The ATC model allows each author to be associated with a topic distribution and a community distribution as its model parameters. A learning algorithm based on variational inference is derived for the model parameter estimation where the two distributions are essentially reinforcing each other during the estimation. We compare the performance of the ATC model with two related generative models using first synthetic data sets and then real data sets, which include a research community data set, a blog data set, a news-sharing data set, and a microblogging data set. The empirical results obtained confirm that the proposed ATC model outperforms the existing models for tasks such as author interest profiling and author community discovery. We also demonstrate how the inferred ATC model can be used to characterize the roles of users/authors in online communities.

Original languageEnglish
Pages (from-to)359-383
Number of pages25
JournalKnowledge and Information Systems
Issue number2
Publication statusPublished - 22 Aug 2015

Scopus Subject Areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence

User-Defined Keywords

  • Author community discovery
  • Author interest profiling
  • Graphical models
  • Variational inference


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