biblioNetwork {bibliometrix}

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,
  remove.terms = NULL,
  synonyms = NULL
)

Arguments

M

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".

n

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.

remove.terms

is a character vector. It contains a list of additional terms to delete from the documents before term extraction. The default is remove.terms = NULL.

synonyms

is a character vector. Each element contains a list of synonyms, separated by ";", that will be merged into a single term (the first word contained in the vector element). The default is synonyms = NULL.

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.

Value

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

See Also

convert2df to import and convert a SCOPUS and Thomson Reuters' ISI Web of Knowledge export file in a data frame.

cocMatrix to compute a co-occurrence matrix.

biblioAnalysis to perform a bibliometric analysis.

Examples

# 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)