Dynamic infinite mixed-membership stochastic blockmodel

Xuhui Fan, Longbing Cao, Richard Yi Da Xu

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2072-2085
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume26
Issue number9
DOIs
Publication statusPublished - Sept 2015
Externally publishedYes

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