The goal of biblionetwork is to provide functions to create bibliometric networks like bibliographic coupling network, co-citation network and co-authorship network. It identifies edges and calculates the weights according to different methods, depending on the type of networks, the type of nodes, and what you want to analyse. These functions are optimized to be used on very large dataset.

The original function, which uses data.table (Dowle and Srinivasan 2020) and allows the user to find edges and calculate weights for large networks, was developed by François Claveau. The different functions in this package have been developed, from Claveau’s original idea, by Alexandre Truc and Aurélien Goutsmedt. The package is maintained by Aurélien Goutsmedt.[1]

You can this package from CRAN:

# Install release version from CRAN
install.packages("biblionetwork")
#> Installing package into 'C:/Users/Public/Documents/Wondershare/CreatorTemp/Rtmp8mTY6s/temp_libpath52848cb50c1'
#> (as 'lib' is unspecified)
#> installing the source package 'biblionetwork'

You can cite this package as:

citation("biblionetwork")
#>
#> To cite biblionetwork in publications use:
#>
#>   Aurélien Goutsmedt, François Claveau and Alexandre Truc (2021).
#>   biblionetwork: A Package For Creating Different Types of Bibliometric
#>   Networks. R package version 0.0.0.9000.
#>   https://github.com/agoutsmedt/biblionetwork
#>
#> A BibTeX entry for LaTeX users is
#>
#>   @Manual{,
#>     title = {biblionetwork: A Package For Creating Different Types of Bibliometric Networks},
#>     author = {Aurélien Goutsmedt and François Claveau and Alexandre Truc},
#>     year = {2021},
#>     note = {R package version 0.0.0.9000},
#>     url = {https://github.com/agoutsmedt/biblionetwork},
#>   }
#>
#> As biblionetwork is continually evolving, you may want to cite its
#> version number. Find it with 'help(package=biblionetwork)'.

## Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("agoutsmedt/biblionetwork")

## Example

The basic function of the package is the biblio_coupling() function. This function calculates the number of references that different articles share together, as well as the coupling angle value of edges in a bibliographic coupling network (Sen and Gan 1983). What you need is just a file with entities (documents, authors, universities, etc.) citing references.[2] See the vignette("Using_biblionetwork") for a more in-depth presentation of the package.

This example use the data incorporated in the package.

library(biblionetwork)

biblio_coupling(Ref_stagflation,
source = "Citing_ItemID_Ref",
ref = "ItemID_Ref",
normalized_weight_only = FALSE,
weight_threshold = 1)
#>             from         to     weight nb_shared_references     Source
#>    1:     214927    2207578 0.14605935                    4     214927
#>    2:     214927    5982867 0.04082483                    1     214927
#>    3:     214927    8456979 0.09733285                    3     214927
#>    4:     214927   10729971 0.29848100                    7     214927
#>    5:     214927   16008556 0.04714045                    1     214927
#>   ---
#> 2712: 1111111161 1111111172 0.03434014                    1 1111111161
#> 2713: 1111111161 1111111180 0.02003610                    1 1111111161
#> 2714: 1111111161 1111111183 0.04050542                    2 1111111161
#> 2715: 1111111172 1111111180 0.03646625                    1 1111111172
#> 2716: 1111111182 1111111183 0.27060404                    8 1111111182
#>           Target
#>    1:    2207578
#>    2:    5982867
#>    3:    8456979
#>    4:   10729971
#>    5:   16008556
#>   ---
#> 2712: 1111111172
#> 2713: 1111111180
#> 2714: 1111111183
#> 2715: 1111111180
#> 2716: 1111111183

## Incorporated data

The biblionetwork package contains bibliometric data built by Goutsmedt (2021). These data gather the academic articles and books, published between 1975 and 2013, that endeavoured to explain the United States stagflation of the 1970s. They also gather all the references cited by these articles and books on stagflation. The Nodes_stagflation.rda file contains information about the academic articles and books on stagflation (the staflation documents), as well as about the references cited at least by two of these stagflation documents. The Ref_stagflation.rda is a data frame of direct citations, with the identifiers of citing documents, and the identifiers of cited documents. The Authors_stagflation.rda is a data frame with the list of documents explaining the US stagflation, and all the authors of these documents (Nodes_stagflation.rda just takes the first author for each document).

## References

Dowle, Matt, and Arun Srinivasan. 2020. Data.table: Extension of ‘Data.frame‘. https://CRAN.R-project.org/package=data.table.
Goutsmedt, Aurélien. 2021. “From the Stagflation to the Great Inflation: Explaining the US Economy of the 1970s.” Revue d’Economie Politique Forthcoming. https://aurelien-goutsmedt.com/media/pdf/stagflation-great-inflation.pdf.
Sen, Subir K., and Shymal K. Gan. 1983. “A Mathematical Extension of the Idea of Bibliographic Coupling and Its Applications.” Annals of Library Science and Documentation 30 (2). http://nopr.niscair.res.in/bitstream/123456789/28008/1/ALIS%2030(2)%2078-82.pdf.

[1] Contact: .

[2] If you want to build a coupling network with entities larger than a document (meaning entities that have published several documents, and thus can cite a reference several times), we rather recommend the use of the coupling_entity() function. See the vignette("Using_biblionetwork") for examples.