TY - GEN
T1 - Multiplex topic models
AU - Yang, Juan
AU - Zeng, Jia
AU - CHEUNG, Kwok Wai
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Belief propagation
KW - Factor graph
KW - Multiplex topic models
UR - http://www.scopus.com/inward/record.url?scp=84893626865&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37453-1_47
DO - 10.1007/978-3-642-37453-1_47
M3 - Conference proceeding
AN - SCOPUS:84893626865
SN - 9783642374524
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 568
EP - 582
BT - Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings
T2 - 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Y2 - 14 April 2013 through 17 April 2013
ER -