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,
  remove.terms = NULL,
  synonyms = NULL
)

Value

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

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.

Examples

Run this code

# 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)

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning