Construct Networks of Different Tags

Usage

simplified_network(
  M,
  from = NULL,
  to = NULL,
  nNode = 30,
  remove_keyword = NULL,
  edge_weight_cutoff = 1,
  analysis,
  network,
  field,
  delete_isolate = TRUE,
  graph = FALSE,
  ...
)

country_network(
  M,
  analysis = "collaboration",
  network = "countries",
  field = "AU_CO_NR",
  edge_weight_cutoff = 5,
  nNode = 20,
  graph = FALSE,
  ...
)

author_network(
  M,
  analysis = "collaboration",
  network = "authors",
  field = "AU",
  edge_weight_cutoff = 5,
  nNode = 200,
  graph = FALSE,
  ...
)

university_network(
  M,
  analysis = "collaboration",
  network = "universities",
  field = "AU_UN_NR",
  edge_weight_cutoff = 10,
  nNode = 30,
  graph = FALSE,
  ...
)

keyword_network(
  M,
  nNode = 100,
  edge_weight_cutoff = 3,
  field = "ID",
  analysis = "co-occurrences",
  network = "keywords",
  graph = FALSE,
  ...
)

Arguments

M: bibliometrix data frame
from: start of PY
to: end of PY
nNode: Maximum number of nodes presented in the network
remove_keyword: regex used to filter data
edge_weight_cutoff: edge weight cutoff
analysis: type of analysis
network: type of network
field: data column for network construction
delete_isolate: TRUE
graph: whether return graph
...: pass to biblio_network()

Value

visNetwork object

Examples

if (FALSE) { # \dontrun{
library("bibliometrixData")
data("garfield")
author_network(garfield)
} # }