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Mixed-membership of experts stochastic blockmodel

Published online by Cambridge University Press:  16 December 2015

ARTHUR WHITE  and
THOMAS BRENDAN MURPHY
ARTHUR WHITE
Affiliation:
School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, Ireland (e-mail: [email protected])
THOMAS BRENDAN MURPHY
Affiliation:
School of Mathematical Sciences, University College Dublin, Dublin 4, Ireland (e-mail: [email protected])
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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.

Keywords

stochastic blockmodelmixed membership modelnode attributescommunity findingmodel based clusteringcovariate informationsocial selection model
Type
Research Article
Information
Network Science , Volume 4 , Issue 1 , March 2016 , pp. 48 - 80
Copyright
Copyright © Cambridge University Press 2015 

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