Compute Bootstrap Centrality Statistics from Bootstrap Samples

This function takes bootstrap samples of graph data from bootstrap_ functions and computes specified centrality statistics for each sample.

Usage

get_bootstrap_centrality(
  boot.result,
  func.name,
  package.name = NULL,
  func.args = NULL,
  obj.type = NULL
)

Arguments

boot.result: A list of bootstrap samples, where each sample represents a graph in a specified format (e.g., adjacency matrix, edgelist). This is output from the bootstrap_ function.
func.name: A character string specifying the centrality function to apply (e.g., "degree", "betweenness"). See get_centrality for details.
package.name: An optional character string indicating the package name containing the centrality function. Defaults to NULL (assuming func.name is available). See get_centrality for details.
func.args: An optional list of additional arguments to pass to the centrality function. Defaults to NULL. See get_centrality for details.
obj.type: A character string specifying the type of obj in each bootstrap sample. Options include "igraph", "matrix", "dgCMatrix", or "edgelist". This is the output.type of the bootstrap_ function.

Value

A list of length B, where each element is the bootstrap centrality statistic result from applying the specified centrality statistic function.

Details

Calculates bootstrap centrality statistics from give bootstrap samples and a specified centrality statistic.

Examples

library(igraph)
data("karate")
set.seed(89)
boot.result <- bootstrap_latent_space(
  karate, d = 2, B = 10)

#Calculate degree centrality (from igraph)
central.result <- get_bootstrap_centrality(boot.result,
                                           func.name = "degree",
                                           package.name = "igraph",
                                           func.args = list(normalized = TRUE))
central.result[[1]]
#>      Mr Hi    Actor 2    Actor 3    Actor 4    Actor 5    Actor 6    Actor 7 
#> 0.33333333 0.30303030 0.24242424 0.18181818 0.09090909 0.09090909 0.09090909 
#>    Actor 8    Actor 9   Actor 10   Actor 11   Actor 12   Actor 13   Actor 14 
#> 0.18181818 0.24242424 0.06060606 0.09090909 0.06060606 0.09090909 0.27272727 
#>   Actor 15   Actor 16   Actor 17   Actor 18   Actor 19   Actor 20   Actor 21 
#> 0.12121212 0.06060606 0.03030303 0.09090909 0.03030303 0.06060606 0.09090909 
#>   Actor 22   Actor 23   Actor 24   Actor 25   Actor 26   Actor 27   Actor 28 
#> 0.12121212 0.18181818 0.18181818 0.12121212 0.06060606 0.09090909 0.06060606 
#>   Actor 29   Actor 30   Actor 31   Actor 32   Actor 33     John A 
#> 0.21212121 0.12121212 0.06060606 0.18181818 0.36363636 0.33333333 

#plot bootstrap centralities vs original centrality
hist(unlist(central.result), freq = FALSE,
     xlab = "degree centrality", main = "Bootstrap Degree Centralities")
lines(density(igraph::degree(karate, normalized = TRUE)), col = "red")