http://dx.doi.org/10.1109/WI-IAT.2010.20">
 

Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Coauthor network topic models with application to expert finding

Language

English

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. © 2010 IEEE.

Keywords

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

Publication Date

2010

Source Publication Title

Proceedings: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology—Workshops

Start Page

366

End Page

373

Conference Location

Toronto, Canada

Publisher

IEEE

ISBN (print)

9781424484829

ISBN (electronic)

9780769541914

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