Department of Computer Science
Multiplex topic models
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.
Belief propagation, Factor graph, Multiplex topic models
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
Gold Coast, Australia
Link to Publisher's Edition
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.