Node centrality

Node centrality

Usage

net_centralities(
  x,
  measures = c("strength", "expected_influence"),
  centrality_fn = NULL,
  ...
)

Arguments

x

A psychnet object or a weighted adjacency matrix.

measures

Character vector of measures to return. Any of "strength", "expected_influence" (the defaults, recommended for psychometric networks), "betweenness", "closeness", plus any names supplied via centrality_fn. Betweenness and closeness are computed on the absolute, inverted-weight graph and are not generally meaningful on signed networks – request them only when a downstream comparison needs them.

centrality_fn

Optional function taking the weighted adjacency matrix and returning a named list of node-centrality vectors, used to supply any measures not built in.

...

Unused.

Value

A tidy data.frame, one row per node, with a node column and one column per requested measure (strength = sum of absolute edge weights, expected_influence = sum of signed edge weights, by default).

Examples

S <- 0.4^abs(outer(1:6, 1:6, "-"))
net_centralities(ebic_glasso(cor_matrix = S, n = 250))
#>   node  strength expected_influence
#> 1   V1 0.3681824          0.3681824
#> 2   V2 0.7105013          0.7105013
#> 3   V3 0.6846378          0.6846378
#> 4   V4 0.6846378          0.6846378
#> 5   V5 0.7105013          0.7105013
#> 6   V6 0.3681824          0.3681824