Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Multirelational Topic Models

Language

English

Abstract

In this paper we propose the multirelational topic model (MRTM) for multiple types of link modeling such as citation and coauthor links in document networks. In the citation network, the MRTM models the citation link between each pair of documents as a binary variable conditioned on their topic distributions. In the coauthor network, the MRTM models the coauthor link between each pair of authors as a binary variable conditioned on their expertise distributions. The topic discovery is collectively regularized by multiple relations in both citation and coauthor networks. This model can summarize topics from the document network, predict citation links between documents and coauthor links between authors. Efficient inference and learning algorithms are derived based on Gibbs sampling. Experiments demonstrate that the MRTM significantly outperforms other state-of-the-art single-relational link modeling methods for large scientific document networks. © 2009 IEEE.

Keywords

Document networks, Markov random fields, Multirelational link modeling, Topic models

Publication Date

2009

Source Publication Title

Proceedings: The Ninth IEEE International Conference on Data Mining

Start Page

1070

End Page

1075

Conference Location

Miami, United States

Publisher

IEEE

DOI

10.1109/ICDM.2009.88

Link to Publisher's Edition

http://dx.doi.org/10.1109/ICDM.2009.88

ISBN (print)

9781424452422

ISBN (electronic)

9780769538952

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