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 sampled 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("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