Theoretical maximum for eigenvector centralization — centr_eigen_tmax • igraph

See centralize() for a summary of graph centralization.

Usage

centr_eigen_tmax(
  graph = NULL,
  nodes = 0,
  directed = FALSE,
  scale = deprecated()
)

Arguments

graph: The input graph. It can also be NULL, if nodes is given.
nodes: The number of vertices. This is ignored if the graph is given.
directed: logical scalar, whether to consider edge directions during the calculation. Ignored in undirected graphs.
scale: Ignored. Computing eigenvector centralization requires normalized eigenvector centrality scores.

Value

Real scalar, the theoretical maximum (unnormalized) graph eigenvector centrality score for graphs with given vertex count and other parameters.

See also

Other centralization related: centr_betw(), centr_betw_tmax(), centr_clo(), centr_clo_tmax(), centr_degree(), centr_degree_tmax(), centr_eigen(), centralize()

centralization_eigenvector_centrality_tmax().

Examples

# A BA graph is quite centralized
g <- sample_pa(1000, m = 4)
centr_eigen(g, normalized = FALSE)$centralization %>%
  `/`(centr_eigen_tmax(g))
#> [1] 0.9386488
centr_eigen(g, normalized = TRUE)$centralization
#> [1] 0.9386488