文章基本信息

标题：Latent Space Approaches to Community Detection in Dynamic Networks
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作者：Daniel K. Sewell ; Yuguo Chen
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2017
卷号：12
期号：2
页码：351-377
DOI：10.1214/16-BA1000
语种：English
出版社：International Society for Bayesian Analysis
摘要：Embedding dyadic data into a latent space has long been a popular approach to modeling networks of all kinds. While clustering has been done using this approach for static networks, this paper gives two methods of community detection within dynamic network data, building upon the distance and projection models previously proposed in the literature. Our proposed approaches capture the time-varying aspect of the data, can model directed or undirected edges, inherently incorporate transitivity and account for each actor’s individual propensity to form edges. We provide Bayesian estimation algorithms, and apply these methods to a ranked dynamic friendship network and world export/import data.