See centralize()
for a summary of graph centralization.
centr_degree(
graph,
mode = c("all", "out", "in", "total"),
loops = TRUE,
normalized = TRUE
)
The input graph.
This is the same as the mode
argument of
degree()
.
Logical scalar, whether to consider loops edges when calculating the degree.
Logical scalar. Whether to normalize the graph level centrality score by dividing by the theoretical maximum.
A named list with the following components:
The node-level centrality scores.
The graph level centrality index.
The maximum theoretical graph level
centralization score for a graph with the given number of vertices,
using the same parameters. If the normalized
argument was
TRUE
, then the result was divided by this number.
Other centralization related:
centr_betw()
,
centr_betw_tmax()
,
centr_clo()
,
centr_clo_tmax()
,
centr_degree_tmax()
,
centr_eigen()
,
centr_eigen_tmax()
,
centralize()
# A BA graph is quite centralized
g <- sample_pa(1000, m = 4)
centr_degree(g)$centralization
#> [1] 0.1638375
centr_clo(g, mode = "all")$centralization
#> [1] 0.4236249
centr_betw(g, directed = FALSE)$centralization
#> [1] 0.2452183
centr_eigen(g, directed = FALSE)$centralization
#> [1] 0.941744