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Compute Centrality Measures for a Network

Source: R/centrality_measures.R
net_centrality.Rd

Computes centrality measures from a netobject, netobject_group, mcml, or cograph_network. The built-in measures match tna::centralities() without importing tna or igraph. The only intentional default difference is that Diffusion is range-normalized by default.

Usage

net_centrality(
  x,
  measures = NULL,
  loops = FALSE,
  normalize = FALSE,
  invert = TRUE,
  normalize_diffusion = TRUE,
  centrality_fn = NULL,
  ...
)

Arguments

x

A netobject, netobject_group, or cograph_network.

measures

Character vector. Centrality measures to compute. Defaults to c("InStrength", "Betweenness", "Diffusion"). Pass "all" for every built-in measure: "OutStrength", "InStrength", "ClosenessIn", "ClosenessOut", "Closeness", "Betweenness", "BetweennessRSP", "Diffusion", and "Clustering". The legacy aliases "InCloseness" and "OutCloseness" are also accepted.

loops

Logical. Include self-loops (diagonal) in computation? Default: FALSE.

normalize

Logical. Range-normalize all requested measures using the same transformation as tna::centralities(normalize = TRUE). Default: FALSE.

invert

Logical. Invert weights for shortest-path measures? Default: TRUE, matching tna.

normalize_diffusion

Logical. Range-normalize Diffusion even when normalize = FALSE. Default: TRUE.

centrality_fn

Optional function. Custom centrality function that takes a weight matrix and returns a named list of centrality vectors.

...

Additional arguments (ignored).

Value

For a netobject: a net_centrality data frame with node names as rows, a state column, and one column per centrality measure. For a netobject_group: a net_centrality_group list of such data frames.

Examples

seqs <- data.frame(
  V1 = c("A","B","A","C"), V2 = c("B","C","B","A"),
  V3 = c("C","A","C","B"))
net <- build_network(seqs, method = "relative")
net_centrality(net)
#> centralities computed excluding loops (diagonal). Pass `loops = TRUE` to include self-transitions.
#>   state InStrength Betweenness Diffusion
#> A     A          1           1       NaN
#> B     B          1           1       NaN
#> C     C          1           1       NaN