Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Multiplex topic models

Language

English

Abstract

Multiplex document networks have multiple types of links such as citation and coauthor links between scientific papers. Inferring thematic topics from multiplex document networks requires quantifying and balancing the influence from different types of links. It is therefore a problem of considerable interest and represents significant challenges. To address this problem, we propose a novel multiplex topic model (MTM) that represents the topic influence from different types of links using a factor graph. To estimate parameters in MTM, we also develop an approximate inference algorithm, multiplex belief propagation (MBP), which can estimate the influence weights of multiple links automatically at each learning iteration. Experimental results confirm the superiority of MTM in two applications, document clustering and link prediction, when compared with several state-of-the-art link-based topic models. © Springer-Verlag 2013.

Keywords

Belief propagation, Factor graph, Multiplex topic models

Publication Date

2013

Source Publication Title

Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I

Start Page

568

End Page

582

Conference Location

Gold Coast, Australia

Publisher

Springer

DOI

10.1007/978-3-642-37453-1_47

ISBN (print)

9783642374524

ISBN (electronic)

9783642374531

This document is currently not available here.

Share

COinS