missSBM: An R Package for Handling Missing Values in the Stochastic Block Model
Dublin Core
Title
missSBM: An R Package for Handling Missing Values in the Stochastic Block Model
Subject
: network, missing data, stochastic block model
Description
The stochastic block model is a popular probabilistic model for random graphs. It is
commonly used to cluster network data by aggregating nodes that share similar connectivity patterns into blocks. When fitting a stochastic block model to a partially observed
network, it is important to consider the underlying process that generates the missing
values, otherwise the inference may be biased. This paper presents missSBM, an R package that fits stochastic block models when the network is partially observed, i.e., the
adjacency matrix contains not only 1s or 0s encoding the presence or absence of edges,
but also NAs encoding the missing information between pairs of nodes. This package implements a set of algorithms to adjust the binary stochastic block model, possibly in the
presence of external covariates, by performing variational inference suitable for several
observation processes. Our implementation automatically explores different block numbers to select the most relevant model according to the integrated classification likelihood
criterion. The integrated classification likelihood criterion can also help determine which
observation process best fits 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 dataset consisting of interactions between political blogs sampled during the
2007 French presidential election
commonly used to cluster network data by aggregating nodes that share similar connectivity patterns into blocks. When fitting a stochastic block model to a partially observed
network, it is important to consider the underlying process that generates the missing
values, otherwise the inference may be biased. This paper presents missSBM, an R package that fits stochastic block models when the network is partially observed, i.e., the
adjacency matrix contains not only 1s or 0s encoding the presence or absence of edges,
but also NAs encoding the missing information between pairs of nodes. This package implements a set of algorithms to adjust the binary stochastic block model, possibly in the
presence of external covariates, by performing variational inference suitable for several
observation processes. Our implementation automatically explores different block numbers to select the most relevant model according to the integrated classification likelihood
criterion. The integrated classification likelihood criterion can also help determine which
observation process best fits 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 dataset consisting of interactions between political blogs sampled during the
2007 French presidential election
Creator
Pierre Barbillon
Source
https://www.jstatsoft.org/article/view/v101i12
Publisher
University Paris-Saclay,
AgroParisTech, INRAE
AgroParisTech, INRAE
Date
January 2022
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Pierre Barbillon, “missSBM: An R Package for Handling Missing Values in the Stochastic Block Model,” Repository Horizon University Indonesia, accessed April 12, 2025, https://repository.horizon.ac.id/items/show/8246.