bibliometrix (version 1.4)

biblioNetwork: Creating Bibliographic networks

Description

biblioNetwork creates different bibliographic networks from a bibliographic data frame.

Usage

biblioNetwork(M, analysis = "coupling", network = "authors", sep = ";")

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 Thomson Reuters' ISI Web of Knowledge file.

analysis

is a character object. It indicates the type of analysis have to 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 aurgument can be "authors", "references", "sources", "countries","keywords" or "author_keywords". Default is network = "authors".

sep

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

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 Batagely and Cerinsek (2013). Typical networks output of biblioNetwork are: #### Collaboration Networks ############ -- Authors collaboration (analysis = "collaboration", network = "authors") -- University collaboration (analysis = "collaboration", network = universities") -- Country collabortion (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")

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

library(igraph)
data(scientometrics)

NetMatrix <- biblioNetwork(scientometrics, analysis = "collaboration", 
network = "authors", sep = ";")
netDegree <- 2
diag <- Matrix::diag 
NetMatrix <- NetMatrix[diag(NetMatrix) >= netDegree,diag(NetMatrix) >= netDegree]
diag(NetMatrix) <- 0

bsk.network <- graph.adjacency(NetMatrix,mode = "undirected")
plot(bsk.network,layout = layout.fruchterman.reingold, vertex.label.dist = 0.5,
vertex.frame.color = 'blue', vertex.label.color = 'black',
vertex.label.font = 1, vertex.label = V(bsk.network)$name, vertex.label.cex = 0.7)


# EXAMPLE 2: Co-citation network

library(igraph)
data(scientometrics)

NetMatrix <- biblioNetwork(scientometrics, analysis = "co-citation", 
network = "references", sep = ";")
netDegree=10
diag <- Matrix::diag
NetMatrix <- NetMatrix[diag(NetMatrix) >= netDegree,diag(NetMatrix) >= netDegree]
diag(NetMatrix) <- 0

bsk.network <- graph.adjacency(NetMatrix,mode = "undirected")
plot(bsk.network,layout = layout.fruchterman.reingold, vertex.label.dist = 0.5,
vertex.frame.color = 'blue', vertex.label.color = 'black',
vertex.label.font = 1, vertex.label = V(bsk.network)$name, vertex.label.cex = 0.7)