TY - JOUR
T1 - Dynamic infinite mixed-membership stochastic blockmodel
AU - Fan, Xuhui
AU - Cao, Longbing
AU - Xu, Richard Yi Da
PY - 2015/9
Y1 - 2015/9
N2 - Directional and pairwise measurements are often used to model interactions in a social network setting. The mixed-membership stochastic blockmodel (MMSB) was a seminal work in this area, and its ability has been extended. However, models such as MMSB face particular challenges in modeling dynamic networks, for example, with the unknown number of communities. Accordingly, this paper proposes a dynamic infinite mixed-membership stochastic blockmodel, a generalized framework that extends the existing work to potentially infinite communities inside a network in dynamic settings (i.e., networks are observed over time). Additional model parameters are introduced to reflect the degree of persistence among one's memberships at consecutive time stamps. Under this framework, two specific models, namely mixture time variant and mixture time invariant models, are proposed to depict two different time correlation structures. Two effective posterior sampling strategies and their results are presented, respectively, using synthetic and real-world data.
AB - Directional and pairwise measurements are often used to model interactions in a social network setting. The mixed-membership stochastic blockmodel (MMSB) was a seminal work in this area, and its ability has been extended. However, models such as MMSB face particular challenges in modeling dynamic networks, for example, with the unknown number of communities. Accordingly, this paper proposes a dynamic infinite mixed-membership stochastic blockmodel, a generalized framework that extends the existing work to potentially infinite communities inside a network in dynamic settings (i.e., networks are observed over time). Additional model parameters are introduced to reflect the degree of persistence among one's memberships at consecutive time stamps. Under this framework, two specific models, namely mixture time variant and mixture time invariant models, are proposed to depict two different time correlation structures. Two effective posterior sampling strategies and their results are presented, respectively, using synthetic and real-world data.
UR - http://www.scopus.com/inward/record.url?scp=84939800171&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/arc/DP130102691
UR - http://purl.org/au-research/grants/arc/LP140100937
U2 - 10.1109/TNNLS.2014.2369374
DO - 10.1109/TNNLS.2014.2369374
M3 - Article
C2 - 25438327
SN - 2162-237X
VL - 26
SP - 2072
EP - 2085
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 9
ER -