Dynamic Infinite Mixed-Membership Stochastic Blockmodel
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 ne...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2015-09, Vol.26 (9), p.2072-2085 |
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| container_title | IEEE transaction on neural networks and learning systems |
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| creator | Xuhui Fan Longbing Cao Da Xu, Richard Yi |
| description | 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. |
| doi_str_mv | 10.1109/TNNLS.2014.2369374 |
| format | Article |
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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. 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| subjects | Bayes methods Bayesian nonparametric Communities Data models dynamic Economic models Gibbs sampling Hidden Markov models Learning systems Markov Chain Monte Carlo (MCMC) inference mixed-membership stochastic blockmodel (MMSB) Peer-to-peer computing slice sampling Stochastic processes |
| title | Dynamic Infinite Mixed-Membership Stochastic Blockmodel |
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