Theoretical maximum for eigenvector centralization

See centralize() for a summary of graph centralization.

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()

Related documentation in the C library

centralization_eigenvector_centrality_tmax().

Examples

# A BA graph is quite centralized
g <- sample_pa(1000, m = 4)
centr_eigen(g, normalized = FALSE)$centralization %>%
  `/`(centr_eigen_tmax(g))
#> [1] 0.9468524
centr_eigen(g, normalized = TRUE)$centralization
#> [1] 0.9468524