missSBM-package: missSBM: Handling Missing Data in Stochastic Block Models

In jchiquet/missSBM: Handling Missing Data in Stochastic Block Models

missSBM: Handling Missing Data in Stochastic Block Models

Description

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', presented in 'Barbillon, Chiquet and Tabouy' (2022) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v101.i12")}, adjusts the popular stochastic block model from network data sampled under various missing data conditions, as described in 'Tabouy, Barbillon and Chiquet' (2019) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2018.1562934")}.

The missSBM package provides the following top-level functions functions:

Details

• observeNetwork a function to draw a partially observe network from an existing, fully observed network according to a variety of sampling designs

• estimateMissSBM a function to perform inference of SBM from a partially observed under various sampling designs.

These function leads to the manipulation of a variety of R objects instantiated from some R6 classes, with their respective fields and methods. They are all generated by the top-level functions itemized above, so that the user should generally not use their constructor or internal methods directly. The user should only have a basic understanding of the fields of each object to manipulate the output in R. The main objects are the following:

• missSBM_fit an object that put together an SBM fit and and network sampling fit - the main point of the missSBM package !

• missSBM_collection an object to store a collection of missSBM_fit, ordered by number of block

• SimpleSBM_fit_MNAR an object to define and store an SBM fit with MNAR values

• SimpleSBM_fit_noCov an object to define and store an SBM fit without covariate, MAR values

• SimpleSBM_fit_withCov an object to define and store an SBM fit with covariates, MAR values

• networkSampling an object to define and store a network sampling fit

missSBM extends some functionality of the package sbm, by inheriting from classes and methods associated to simple stochastic block models.

Author(s)

Maintainer: Julien Chiquet [e-mail] (ORCID)

Authors:

• Pierre Barbillon [e-mail] (ORCID)

• Timothée Tabouy [e-mail]

Other contributors:

• Jean-Benoist Léger [e-mail] (provided C++ implementaion of K-means) [contributor]

• François Gindraud [e-mail] (provided C++ interface to NLopt) [contributor]

• großBM team [contributor]

Pierre Barbillon [e-mail]

Julien Chiquet [e-mail]

Timothée Tabouy [e-mail]

References

• Pierre Barbillon, Julien Chiquet & Timothée Tabouy (2022) "missSBM: An R Package for Handling Missing Values in the Stochastic Block Model", Journal of Statistical Software, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v101.i12")}

• Timothée Tabouy, Pierre Barbillon & Julien Chiquet (2019) “Variational Inference for Stochastic Block Models from Sampled Data”, Journal of the American Statistical Association, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2018.1562934")}

See Also

Useful links:

• https://grosssbm.github.io/missSBM/

• Report bugs at https://github.com/grossSBM/missSBM/issues

jchiquet/missSBM documentation built on Oct. 25, 2023, 5:30 a.m.