centr_eigen_tmax: Theoretical maximum for eigenvector centralization

In igraph/rigraph: Network Analysis and Visualization

Theoretical maximum for eigenvector centralization

Description

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.

Related documentation in the C library

centralization_eigenvector_centrality_tmax().

See Also

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

Examples

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

igraph/rigraph documentation built on June 13, 2025, 1:44 p.m.