Mixed membership of experts stochastic blockmodel
Citation:
Arthur White and Thomas Brendan Murphy, Mixed membership of experts stochastic blockmodel, Network Science, Volume 4, Issue 01, 2016, 48 - 80Abstract:
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 con-
ditional 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 (MMSBM)
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 that 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 MMSBM that 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
Sponsor
Grant Number
SFI stipend
12/RC/2289
SFI stipend
08/SRC/I1407
Author's Homepage:
http://people.tcd.ie/arwhiteDescription:
PUBLISHED
Author: WHITE, ARTHUR
Type of material:
Journal ArticleCollections
Series/Report no:
Network ScienceVolume 4
Issue 01
Availability:
Full text availableKeywords:
Social network analysis (SNA)Subject (TCD):
CLUSTERING , CLUSTERS , Mixed-membership models , SOCIAL NETWORKS , Social Network Analysis , model based clusteringDOI:
http://dx.doi.org/10.1017/nws.2015.29Metadata
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