Linkage-package {Linkage}

Clustering Communication Networks Using the Stochastic Topic Block Model Through Linkage.fr

Description

It allows to cluster communication networks using the Stochastic Topic Block Model <doi:10.1007/s11222-016-9713-7> by posting jobs through the API of the linkage.fr server, which implements the clustering method. The package also allows to visualize the clustering results returned by the server.

Details

The DESCRIPTION file:

Encoding:	UTF-8
Package:	Linkage
Type:	Package
Title:	Clustering Communication Networks Using the Stochastic Topic Block Model Through Linkage.fr
Version:	0.9
Depends:	R (>= 3.5.0)
Imports:	httr, jsonlite, RColorBrewer, sna, network
Date:	2022-04-08
Author:	Charles Bouveyron, Pierre Latouche, Stéphane Petiot, Carlos Ocanto
Maintainer:	Charles Bouveyron <[e-mail]>
Description:	It allows to cluster communication networks using the Stochastic Topic Block Model <doi:10.1007/s11222-016-9713-7> by posting jobs through the API of the linkage.fr server, which implements the clustering method. The package also allows to visualize the clustering results returned by the server.
License:	GPL-3

Index of help topics:

Enron                   The Enron email network
Linkage-package         Clustering Communication Networks Using the
                        Stochastic Topic Block Model Through Linkage.fr
linkage.check           Monitor achievment of the current job
linkage.getresults      Retrieve results for a specific job.
linkage.post            Post a job on Linkage.fr to cluster a network
                        with STBM
plot.linkage            The plot function for 'linkage' objects.

It allows to cluster communication networks using the Stochastic Topic Block Model (Bouveyron et al., 2018, <doi:10.1007/s11222-016-9713-7>) by posting jobs through the API of the linkage.fr server, which implements the clustering method. The package also allows to visualize the clustering results returned by the server.

Author(s)

Charles Bouveyron, Pierre Latouche, Stéphane Petiot, Carlos Ocanto

Maintainer: Charles Bouveyron <[e-mail]>

References

C. Bouveyron, P. Latouche and R. Zreik, The Stochastic Topic Block Model for the Clustering of Networks with Textual Edges, Statistics and Computing, vol. 28(1), pp. 11-31, 2017 <doi:10.1007/s11222-016-9713-7>

Examples

## Not run: 
data(Enron)
write.table(Enron, file="Enron.csv",row.names=FALSE,col.names=FALSE, sep=",")
file = "Enron.csv"

# Provide the user token, which is provided on "developers" page
# of http://linkage.fr (after registration)
token = "xxxxxxxxxxxxxxxxxxxx"

# Post the job
job_id = linkage.post(file, token, job_title="My job: Enron",
                      clusters_min = 8, clusters_max = 8,
                      topics_min = 6,topics_max = 6,
                      filter_largest_subgraph = TRUE)

# Monitor achievment of the current job
ans = linkage.check(token)

# Retrieve results (once achievment is 100
res = linkage.getresults(job_id,token)

# Plot the results
plot(res,type='all')

## End(Not run)