biblioNetwork: Creating Bibliographic networks

Description

biblioNetwork creates different bibliographic networks from a bibliographic data frame.

Usage

biblioNetwork(
  M,
  analysis = "coupling",
  network = "authors",
  n = NULL,
  sep = ";",
  short = FALSE,
  shortlabel = TRUE
)

Arguments

is a bibliographic data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to manuscripts and variables to Field Tag in the original SCOPUS and Clarivate Analytics WoS file.

analysis

is a character object. It indicates the type of analysis can be performed. analysis argument can be "collaboration", "coupling", "co-occurrences" or "co-citation". Default is analysis = "coupling".

network

is a character object. It indicates the network typology. The network argument can be "authors", "references", "sources", "countries","keywords", "author_keywords", "titles", or "abstracts". Default is network = "authors".

is an integer. It indicates the number of items to select. If N = NULL, all items are selected.

sep

is the field separator character. This character separates strings in each column of the data frame. The default is sep = ";".

short

is a logical. If TRUE all items with frequency<2 are deleted to reduce the matrix size.

shortlabel

is logical. IF TRUE, reference labels are stored in a short format. Default is shortlabel=TRUE.

Value

It is a squared network matrix. It is an object of class dgMatrix of the package Matrix.

Details

The function biblioNetwork can create a collection of bibliographic networks following the approach proposed by Batagelj & Cerinsek (2013) and Aria & cuccurullo (2017). Typical networks output of biblioNetwork are: #### Collaboration Networks ############ -- Authors collaboration (analysis = "collaboration", network = "authors") -- University collaboration (analysis = "collaboration", network = universities") -- Country collaboration (analysis = "collaboration", network = "countries") #### Co-citation Networks ############## -- Authors co-citation (analysis = "co-citation", network = "authors") -- Reference co-citation (analysis = "co-citation", network = "references") -- Source co-citation (analysis = "co-citation", network = "sources") #### Coupling Networks ################ -- Manuscript coupling (analysis = "coupling", network = "references") -- Authors coupling (analysis = "coupling", network = "authors") -- Source coupling (analysis = "coupling", network = "sources") -- Country coupling (analysis = "coupling", network = "countries") #### Co-occurrences Networks ################ -- Authors co-occurrences (analysis = "co-occurrences", network = "authors") -- Source co-occurrences (analysis = "co-occurrences", network = "sources") -- Keyword co-occurrences (analysis = "co-occurrences", network = "keywords") -- Author-Keyword co-occurrences (analysis = "co-occurrences", network = "author_keywords") -- Title content co-occurrences (analysis = "co-occurrences", network = "titles") -- Abstract content co-occurrences (analysis = "co-occurrences", network = "abstracts")

References: Batagelj, V., & Cerinsek, M. (2013). On bibliographic networks. Scientometrics, 96(3), 845-864. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.

Examples

Run this code

# NOT RUN {
# EXAMPLE 1: Authors collaboration network

# data(scientometrics, package = "bibliometrixData")

# NetMatrix <- biblioNetwork(scientometrics, analysis = "collaboration", 
# network = "authors", sep = ";")

# net <- networkPlot(NetMatrix, n = 30, type = "kamada", Title = "Collaboration",labelsize=0.5) 


# EXAMPLE 2: Co-citation network

data(scientometrics, package = "bibliometrixData")

NetMatrix <- biblioNetwork(scientometrics, analysis = "co-citation", 
network = "references", sep = ";")

net <- networkPlot(NetMatrix, n = 30, type = "kamada", Title = "Co-Citation",labelsize=0.5) 

# }

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