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) 10.18637/jss.v101.i12, 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.
The Last CRAN version is available via
install.packages("missSBM")The development version is available via
devtools::install_github("grossSBM/missSBM")Please cite our work using the following references:
Barbillon, P., Chiquet, J., & Tabouy, T. (2022). missSBM: An R Package for Handling Missing Values in the Stochastic Block Model. Journal of Statistical Software, 101(12), 1–32. 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, DOI: 10.1080/01621459.2018.1562934