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.
Keywords
Belief propagation, Factor graph, Multiplex topic models
Publication Date
4-2013
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
Editors
Pei, Jian ; Tseng, Vincent S. ; Cao, Longbing ; Motoda, Hiroshi ; Xu, Guandong
Start Page
568
End Page
582
Series Title
Lecture notes in computer science, 7818.
Conference Location
Gold Coast, Australia
Publisher
Springer
Peer Reviewed
1
Copyright
© Springer-Verlag Berlin Heidelberg 2013
Funder
This work is supported by NSFC (Grant No. 61003154), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 12KJA520004), and a grant from Baidu to JZ, and General Research Fund (HKBU210410) from the Research Grant Council of the Hong Kong Special Administrative Region, China to WKC.
DOI
10.1007/978-3-642-37453-1_47
Link to Publisher's Edition
http://dx.doi.org/10.1007/978-3-642-37453-1_47
ISBN (print)
9783642374524
ISBN (electronic)
9783642374531
Recommended Citation
Yang, Juan, Jia Zeng, and William K. Cheung. "Multiplex topic models." Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I (2013): 568-582.