TY - JOUR
T1 - Variational Bayesian Inference Algorithms for Infinite Relational Model of Network Data
AU - Konishi, Takuya
AU - Kubo, Takatomi
AU - Watanabe, Kazuho
AU - Ikeda, Kazushi
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2015/9/1
Y1 - 2015/9/1
N2 - Network data show the relationship among one kind of objects, such as social networks and hyperlinks on the Web. Many statistical models have been proposed for analyzing these data. For modeling cluster structures of networks, the infinite relational model (IRM) was proposed as a Bayesian nonparametric extension of the stochastic block model. In this brief, we derive the inference algorithms for the IRM of network data based on the variational Bayesian (VB) inference methods. After showing the standard VB inference, we derive the collapsed VB (CVB) inference and its variant called the zeroth-order CVB inference. We compared the performances of the inference algorithms using six real network datasets. The CVB inference outperformed the VB inference in most of the datasets, and the differences were especially larger in dense networks.
AB - Network data show the relationship among one kind of objects, such as social networks and hyperlinks on the Web. Many statistical models have been proposed for analyzing these data. For modeling cluster structures of networks, the infinite relational model (IRM) was proposed as a Bayesian nonparametric extension of the stochastic block model. In this brief, we derive the inference algorithms for the IRM of network data based on the variational Bayesian (VB) inference methods. After showing the standard VB inference, we derive the collapsed VB (CVB) inference and its variant called the zeroth-order CVB inference. We compared the performances of the inference algorithms using six real network datasets. The CVB inference outperformed the VB inference in most of the datasets, and the differences were especially larger in dense networks.
UR - http://www.scopus.com/inward/record.url?scp=84939856387&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84939856387&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2014.2362012
DO - 10.1109/TNNLS.2014.2362012
M3 - Article
AN - SCOPUS:84939856387
VL - 26
SP - 2176
EP - 2181
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
SN - 2162-237X
IS - 9
M1 - 6937190
ER -