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missSBM: Handling missing data in Stochastic Block Models

When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 due to missing information between node pairs), it is possible to account for the underlying process that generates those NAs. ‘missSBM’ adjusts the popular stochastic block model from network data observed under various missing data conditions, as described in Tabouy, Barbillon and Chiquet (2019) 10.1080/01621459.2018.1562934.

Installation

The Last CRAN version is available via

install.packages("missSBM")

The development version is available via

devtools::install_github("grossSBM/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

0.3.0

License

GPL-3

Maintainer

Julien Chiquet

Last Published

November 18th, 2020

Functions in missSBM (0.3.0)

nodeSampler

Virtual class for all node-centered samplers
networkSamplingNodes_fit

Virtual class used to define a family of networkSamplingNodes_fit
dyadSampling_fit

Class for fitting a dyad sampling
dyadSampler

Virtual class for all dyad-centered samplers
networkSamplingDyads_fit

Virtual class used to define a family of networkSamplingDyads_fit
er_network

ER ego centered network
networkSampling

Definition of R6 Class 'networkSampling'
networkSampler

Definition of R6 Class 'networkSampling_sampler'
predicted.missSBM_fit

Prediction of a missSBM_fit (i.e. network with imputed missing dyads)
estimateMissSBM

Estimation of simple SBMs with missing data
simpleDyadSampler

Class for defining a simple dyad sampler
doubleStandardSampler

Class for defining a double-standard sampler
nodeSampling_fit

Class for fitting a node sampling
doubleStandardSampling_fit

Class for fitting a double-standard sampling
missSBM_collection

An R6 class to represent a collection of SBM fits with missing data
missSBM-defunct

Defunct Functions in Package missSBM
snowballSampler

Class for defining a snowball sampler
summary.missSBM_fit

Summary method for a missSBM_fit
missSBM

Adjusting Stochastic Block Models under various missing data conditions
%>%

Pipe operator
missSBM_fit

An R6 class to represent an SBM fit with missing data
war

War data set
plot.missSBM_fit

Visualization for an object missSBM_fit
fitted.missSBM_fit

Extract model fitted values from object missSBM_fit, return by estimateMissSBM()
blockNodeSampler

Class for defining a block node sampler
frenchblog2007

Political Blogosphere network prior to 2007 French presidential election
partlyObservedNetwork

An R6 Class used for internal representation of a partially observed network
observeNetwork

Observe a network partially according to a given sampling design
simpleNodeSampler

Class for defining a simple node sampler
smooth

Smooth the path ICL in a collection of missSBM_fit models
blockDyadSampling_fit

Class for fitting a block-dyad sampling
covarDyadSampling_fit

Class for fitting a dyad sampling with covariates
degreeSampling_fit

Class for fitting a degree sampling
blockDyadSampler

Class for defining a block dyad sampler
covarNodeSampling_fit

Class for fitting a node-centered sampling with covariate
degreeSampler

Class for defining a degree sampler
blockSampling_fit

Class for fitting a block-node sampling
SimpleSBM_fit_missSBM

This internal class is designed to adjust a binary Stochastic Block Model in the context of missSBM.
coef.missSBM_fit

Extract model coefficients