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missSBM: Handling missing data in Stochastic Block Models

When a network is partially observed (here, missing dyads, that is, entries with NA in the adjacency matrix rather than 1 or 0), it is possible to account for the underlying process that generates those NAs. missSBM is an R package for adjusting the popular Stochastic Block Models from network data sampled under various missing data conditions.

Installation

devtools::install_github("jchiquet/missSBM")

Reference

Please cite our work using the following reference:

Timothée Tabouy, Pierre Barbillon & Julien Chiquet (2019) “Variational Inference for Stochastic Block Models from Sampled Data”, Journal of the American Statistical Association, DOI: 10.1080/01621459.2018.1562934

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Version

Install

install.packages('missSBM')

Monthly Downloads

268

Version

0.2.0

License

GPL-3

Maintainer

Julien Chiquet

Last Published

June 8th, 2019

Functions in missSBM (0.2.0)

missSBM_collection

An object to represent a collection of missSBM_fit
estimate

Estimation of SBMs with missing data
sample

Sampling of network data
war

War data set
smooth

Smooth the path ICL in a collection of missSBM_fit models
frenchblog2007

Political Blogosphere network prior to 2007 French presidential election
dyadSampler

Virtual class for all dyad-centered samplers
simulate

Simulation of an SBM
er_network

ER ego centred network
SBM_fit

R6 Class definition of an SBM-fit
networkSampler

Definition of R6 Class 'networkSampling_sampler'
missSBM_fit

R6 Class definition of an SBM-fit
networkSampling

Definition of R6 Class 'networkSampling'
networkSamplingDyads_fit

Virtual class used to define a family of networkSamplingDyads_fit
SBM_sampler

An R6 Class to represent a sampler for a SBM
networkSamplingNodes_fit

Virtual class used to define a family of networkSamplingNodes_fit
%>%

Pipe operator
missSBM

Adjusting Stochastic Block Models under various missing data conditions
sampledNetwork

An R6 Class to represent sampled network data
prepare_data

Prepare network data for estimation with missing data