build_dynamic_networks: Creating One or Multiple Networks from a List of Nodes and...

In agoutsmedt/networkflow: Functions For A Workflow To Manipulate Networks

Creating One or Multiple Networks from a List of Nodes and Directed Edges

Description

build_network() creates a network from a table of nodes and its directed edges. That is a special case of the more general build_dynamic_networks(). This function creates one or several tibble graphs (built with tidygraph) from a table of nodes and its directed edges. For instance, for bibliometric networks, you can give a list of articles and the list of the references these articles cite. You can use it to build a single network or multiple networks over different time windows.

Usage

build_dynamic_networks(
  nodes,
  directed_edges,
  source_id,
  target_id,
  time_variable = NULL,
  time_window = NULL,
  cooccurrence_method = c("coupling_angle", "coupling_strength", "coupling_similarity"),
  overlapping_window = FALSE,
  edges_threshold = 1,
  compute_size = FALSE,
  keep_singleton = FALSE,
  filter_components = FALSE,
  ...,
  verbose = TRUE
)

build_network(
  nodes,
  directed_edges,
  source_id,
  target_id,
  cooccurrence_method = c("coupling_angle", "coupling_strength", "coupling_similarity"),
  edges_threshold = 1,
  compute_size = FALSE,
  keep_singleton = FALSE,
  filter_components = FALSE,
  ...
)

Arguments

nodes	The table with all the nodes and their metadata. For instance, if your nodes are articles, this table is likely to contain the year of publication, the name of the authors, the title of the article, etc... The table must have one row per node.
directed_edges	The table with of all the elements to which your nodes are connected. If your nodes are articles, the directed_edges table can contain the list of the references cited by these articles, the authors that have written these articles, or the affiliations of the authors of these articles.
source_id	The quoted name of the column with the unique identifier of each node. For instance, for a bibliographic coupling network, the id of your citing documents. It corresponds to the source argument of biblionetwork functions.
target_id	The quoted name of the column with the unique identifier of each element connected to the node (for instance, the identifier of the reference cited by your node if the node is an article). It corresponds to the ref argument of biblionetwork functions.
time_variable	The column with the temporal variable you want to use to build your windows for the succession of networks. By default, time_variable is NULL and the function will only build one network without taking into account any temporal variable.
time_window	The length of your network relatively of the unity of the time_variable column. If you use a variable in years as time_variable and you set time_window at 5, the function will build network on five year windows. By default, time_window is NULL and the function will only build one network.
cooccurrence_method	Choose a cooccurrence method to build your indirect edges table. The function propose three methods that depends on the biblionetwork package and three methods that are implemented in it: the coupling angle measure (see biblionetwork::biblio_coupling() for documentation); the coupling strength measure (biblionetwork::coupling_strength()); the coupling similarity measure (biblionetwork:: coupling_similarity()).
overlapping_window	Set to FALSE by default. If set to TRUE, and if time_variable and time_window not NULL, the function will create a succession of networks for moving time windows. The windows are moving one unit per one unit of the time_variable. For instance, for years, if time_window set to 5, it creates networks for successive time windows like 1970-1974, 1971-1975, 1972-1976, etc.
edges_threshold	Threshold value for building your edges. With a higher threshold, only the stronger links will be kept. See the biblionetwork package documentation and the cooccurrence_method parameter.
compute_size	Set to FALSE by default. If TRUE, the function uses the directed_edges data to calculate how many directed edges a node receives (as a target). If directed_edges is a table of direct citations, the functions calculates the number of time a node is cited by the other nodes. You need to have the target_id in the nodes table to make the link with the targetted nodes in the directed_edges table.
keep_singleton	Set to FALSE by default. If TRUE, the function removes the nodes that have no undirected edges, i.e. no cooccurrence with any other nodes. In graphical terms, these nodes are alone in the network, with no link with other nodes.
filter_components	Set to TRUE if you want to run networkflow::filter_components() to filter the components of the network(s) and keep only the biggest component(s). If you don't change the defaults parameters of networkflow::filter_components(), it will keep only the main component.
...	Additional arguments from networkflow::filter_components().
verbose	Set to FALSE if you don't want the function to display different sort of information.

Details

build_network() has been added for convenience but it is just a special case of the more general build_dynamic_networks(), with

Value

If time_window is NULL, the function computes only one network and return a tidygraph object built with tbl_graph(). If time_variable and time_window are not NULL, the function returns a list of tidygraph networks, for each time window.

Examples

library(networkflow)

nodes <- Nodes_stagflation |>
dplyr::rename(ID_Art = ItemID_Ref) |>
dplyr::filter(Type == "Stagflation")

references <- Ref_stagflation |>
dplyr::rename(ID_Art = Citing_ItemID_Ref)

temporal_networks <- build_dynamic_networks(nodes = nodes,
directed_edges = references,
source_id = "ID_Art",
target_id = "ItemID_Ref",
time_variable = "Year",
cooccurrence_method = "coupling_similarity",
time_window = 20,
edges_threshold = 1,
overlapping_window = TRUE)

temporal_networks[[1]]

agoutsmedt/networkflow documentation built on March 15, 2023, 11:51 p.m.