couplingMap: Coupling Analysis

Description

It performs a coupling network analysis and plots community detection results on a bi-dimensional map (Coupling Map).

Usage

couplingMap(
  M,
  analysis = "documents",
  field = "CR",
  n = 500,
  label.term = NULL,
  ngrams = 1,
  impact.measure = "local",
  minfreq = 5,
  community.repulsion = 0.1,
  stemming = FALSE,
  size = 0.5,
  n.labels = 1,
  repel = TRUE,
  cluster = "walktrap"
)

Value

a list containing:

`map`		The coupling map as ggplot2 object
`clusters`		Centrality and Density values for each cluster.
`data`		A list of units following in each cluster
`nclust`		The number of clusters
`NCS`		The Normalized Citation Score dataframe
`net`		A list containing the network output (as provided from the networkPlot function)

Arguments

M: is a bibliographic dataframe.
analysis: is the textual attribute used to select the unit of analysis. It can be analysis = c("documents", "authors", "sources").
field: is the textual attribute used to measure the coupling strength. It can be field = c("CR", "ID","DE", "TI", "AB").
n: is an integer. It indicates the number of units to include in the analysis.
label.term: is a character. It indicates which content metadata have to use for cluster labeling. It can be label.term = c("ID","DE","TI","AB"). If label.term = NULL cluster items will be use for labeling.
ngrams: is an integer between 1 and 4. It indicates the type of n-gram to extract from texts. An n-gram is a contiguous sequence of n terms. The function can extract n-grams composed by 1, 2, 3 or 4 terms. Default value is ngrams=1.
impact.measure: is a character. It indicates the impact measure used to rank cluster elements (documents, authors or sources). It can be impact.measure = c("local", "global").\ With impact.measure = "local", couplingMap calculates elements impact using the Normalized Local Citation Score while using impact.measure = "global", the function uses the Normalized Global Citation Score to measure elements impact.
minfreq: is a integer. It indicates the minimum frequency (per thousand) of a cluster. It is a number in the range (0,1000).
community.repulsion: is a real. It indicates the repulsion force among network communities. It is a real number between 0 and 1. Default is community.repulsion = 0.1.
stemming: is logical. If it is TRUE the word (from titles or abstracts) will be stemmed (using the Porter's algorithm).
size: is numerical. It indicates the size of the cluster circles and is a number in the range (0.01,1).
n.labels: is integer. It indicates how many labels associate to each cluster. Default is n.labels = 1.
repel: is logical. If it is TRUE ggplot uses geom_label_repel instead of geom_label.
cluster: is a character. It indicates the type of cluster to perform among ("optimal", "louvain","leiden", "infomap","edge_betweenness","walktrap", "spinglass", "leading_eigen", "fast_greedy").

Details

The analysis can be performed on three different units: documents, authors or sources and the coupling strength can be measured using the classical approach (coupled by references) or a novel approach based on unit contents (keywords or terms from titles and abstracts)

The x-axis measures the cluster centrality (by Callon's Centrality index) while the y-axis measures the cluster impact by Mean Normalized Local Citation Score (MNLCS). The Normalized Local Citation Score (NLCS) of a document is calculated by dividing the actual count of local citing items by the expected citation rate for documents with the same year of publication.

Examples

Run this code


if (FALSE) {
data(management, package = "bibliometrixData")
res <- couplingMap(management, analysis = "authors", field = "CR", n = 250, impact.measure="local", 
                   minfreq = 3, size = 0.5, repel = TRUE)
plot(res$map)
}