Mixed-Membership of Experts Stochastic Blockmodel

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Title: Mixed-Membership of Experts Stochastic Blockmodel
Authors: White, Arthur
Murphy, Thomas Brendan
Permanent link: http://hdl.handle.net/10197/8509
Date: Mar-2016
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.
Type of material: Journal Article
Publisher: Cambridge University Press
Copyright (published version): 2015 Cambridge University Press
Keywords: Machine learningStatisticsStochastic blockmodelMixed membership modelNode attributesCommunity findingModel-based clusteringCovariate informationSocial selection model
DOI: 10.1017/nws.2015.29
Language: en
Status of Item: Peer reviewed
Appears in Collections:Insight Research Collection

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