Department of Computer Science
Coauthor network topic models with application to expert finding
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.
Coauthor document network, Expert finding, Gibbs sampling, Higher-order relation, Topic models
Source Publication Title
Proceedings: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology—Workshops
Link to Publisher's Edition
Zeng, Jia, William K. Cheung, Chun-Hung Li, and Jiming Liu. "Coauthor network topic models with application to expert finding." Proceedings: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology—Workshops (2010): 366-373.