  The Stochastic Block Model (SBM) is a popular probabilistic model for random
graphs. It is commonly used for clustering network data by aggregating nodes
that share similar connectivity patterns into blocks. When fitting an SBM to a
network which is partially observed, it is important to take into account the
underlying process that generates the missing values, otherwise the inference
may be biased. This paper introduces missSBM, an R-package fitting the SBM when
the network is partially observed, i.e., the adjacency matrix contains not only
1's or 0's encoding presence or absence of edges but also NA's encoding missing
information between pairs of nodes. This package implements a set of algorithms
for fitting the binary SBM, possibly in the presence of external covariates, by
performing variational inference adapted to several observation processes. Our
implementation automatically explores different block numbers to select the
most relevant model according to the Integrated Classification Likelihood (ICL)
criterion. The ICL criterion can also help determine which observation process
better corresponds to a given dataset. Finally, missSBM can be used to perform
imputation of missing entries in the adjacency matrix. We illustrate the
package on a network data set consisting of interactions between political
blogs sampled during the French presidential election in 2007.
