biblioNetwork: Creating Bibliographic networks
In bibliometrix: Comprehensive Science Mapping Analysis

biblioNetwork

R Documentation

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.

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)

bibliometrix documentation built on July 3, 2024, 5:07 p.m.