'shiny' Application to Use the Stochastic Block Model.
shinySbm is a R package containing a shiny application. This application provides a user-friendly interface for network analysis based on the sbm package made by Chiquet J, Donnet S and Barbillon P (2023) CRAN. The sbm package regroups into a unique framework tools for estimating and manipulating variants of the stochastic block model. shinySbm allows you to easily apply and explore the outputs of a Stochastic Block Model without programming. It is useful if you want to analyze your network data (adjacency matrix or list of edges) without knowing the R language or to learn the basics of the sbm package.
Stochastic block models (SBMs) are probabilistic models in statistical analysis of graphs or networks, that can be used to discover or understand the (hidden/latent) structure of a network, as well as for clustering purposes.
Stochastic Block Models are applied on network to simplify the information they gather, and help visualize the main behaviours/categories/relationships present in your network. It’s a latent model which identify significant blocks (groups) of nodes with similar connectivity patterns. This could help you to know if your network: hides closed sub-communities, is hierarchical, or has another specific structure.
With shinySbm you should also be able to:
- Easily run a Stochastic Block Model (set your model, infer associated parameters and choose the number of blocks)
- Get some nice outputs as matrix and network plots organized by blocks
- Get a summary of the modelling
- Extract lists of nodes associated with their blocks
How to use the application
On Shiny Migale
I you want to use shinySBM without having to code a single line, the app is available on Migale.
With R
Installation
You can install the development version of shinySbm like so:
install.packages("shinySbm")
The shinySbm package should be installed.
Running the application
From a new R session run
shinySbm::shinySbmApp()
With docker
Installation
If you are familiar to docker, you can also download the docker image by running the command:
docker pull registry.forgemia.inra.fr/theodore.vanrenterghem/shinysbm:latest
Running the application
Once installed you can run the command to launch the app:
docker run -p 3838:3838 registry.forgemia.inra.fr/theodore.vanrenterghem/shinysbm:latest
And then from your browser find the address http://localhost:3838/
Contact
Any questions, problems or comments regarding this application ?
Contact us: [email protected]
References
Chiquet J, Donnet S, Barbillon P (2023). sbm: Stochastic Blockmodels. R package version 0.4.5,
https://CRAN.R-project.org/package=sbm.