2014/04/01 by Arthur White, Thomas Brendan Murphy
Mathematics · Physics and Astronomy · Social Sciences · #Artificial intelligence #Complex Network Analysis Techniques #Computational and Text Analysis Methods #Computer science #Covariate #Data mining #Econometrics #Extension (predicate logic) #Flexibility (engineering) #Latent variable #Machine learning #Mathematics #Opinion Dynamics and Social Influence #Set (abstract data type) #Social network analysis #Statistics #Theoretical computer science #stat.CO #stat.ME
paper · pdf · doi:10.1017/nws.2015.29
published as Network Science, 4, pp 48-80 (2016) · 32 pages, 8 figures
arxiv created 2014/04/01 · openalex publication_date 2015/12/16 · arxiv updated 2016/06/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Abstract Social network analysis is the study of how links between a set of actors are formed. Typically, it is believed that links are formed in a structured manner, which may be due to, for example, political or material incentives, and which often may not be directly observable. The stochastic blockmodel represents this structure using latent groups which exhibit different connective properties, so that conditional on the group membership of two actors, the probability of a link being formed between them is represented by a connectivity matrix. The mixed membership stochastic blockmodel extends this model to allow actors membership to different groups, depending on the interaction in question, providing further flexibility. Attribute information can also play an important role in explaining network formation. Network models which do not explicitly incorporate covariate information require the analyst to compare fitted network models to additional attributes in a post-hoc manner. We introduce the mixed membership of experts stochastic blockmodel, an extension to the mixed membership stochastic blockmodel which incorporates covariate actor information into the existing model. The method is illustrated with application to the Lazega Lawyers dataset. Model and variable selection methods are also discussed.