Coauthor network topic models with application to expert finding

Jia Zeng*, Kwok Wai CHEUNG, Chun Hung Li, Jiming LIU

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

This paper presents the coauthor network topic (CNT) model constructed based on Markov random fields (MRFs) with higher-order cliques. Regularized by the complex coauthor network structures, the CNT can simultaneously learn topic distributions as well as expertise of authors from large document collections. Besides modeling the pairwise relations, we model also higher-order coauthor relations and investigate their effects on topic and expertise modeling. We derive efficient inference and learning algorithms from the Gibbs sampling procedure. To confirm the effectiveness, we apply the CNT to the expert finding problem on a DBLP corpus of titles from six different computer science conferences. Experiments show that the higher-order relations among coauthors can improve the topic and expertise modeling performance over the case with pairwise relations, and thus can find more relevant experts given a query topic or document.

Original languageEnglish
Title of host publication2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
Pages366-373
Number of pages8
DOIs
Publication statusPublished - 2010
Event2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010 - Toronto, ON, Canada
Duration: 31 Aug 20103 Sep 2010

Publication series

NameProceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
Volume1

Conference

Conference2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
Country/TerritoryCanada
CityToronto, ON
Period31/08/103/09/10

Scopus Subject Areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software

User-Defined Keywords

  • Coauthor document network
  • Expert finding
  • Gibbs sampling
  • Higher-order relation
  • Topic models

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