Centralize a graph according to the eigenvector centrality of vertices
See centralize for a summary of graph centralization.
centr_eigen( graph, directed = FALSE, scale = TRUE, options = arpack_defaults, normalized = TRUE )
graph |
The input graph. |
directed |
logical scalar, whether to use directed shortest paths for calculating eigenvector centrality. |
scale |
Whether to rescale the eigenvector centrality scores, such that the maximum score is one. |
options |
This is passed to |
normalized |
Logical scalar. Whether to normalize the graph level centrality score by dividing by the theoretical maximum. |
A named list with the following components:
vector |
The node-level centrality scores. |
value |
The corresponding eigenvalue. |
options |
ARPACK options, see the return value of
|
centralization |
The graph level centrality index. |
theoretical_max |
The same as above, the theoretical maximum centralization score for a graph with the same number of vertices. |
Other centralization related:
centr_betw_tmax(),
centr_betw(),
centr_clo_tmax(),
centr_clo(),
centr_degree_tmax(),
centr_degree(),
centr_eigen_tmax(),
centralize()
# A BA graph is quite centralized g <- sample_pa(1000, m = 4) centr_degree(g)$centralization centr_clo(g, mode = "all")$centralization centr_betw(g, directed = FALSE)$centralization centr_eigen(g, directed = FALSE)$centralization # The most centralized graph according to eigenvector centrality g0 <- make_graph(c(2,1), n = 10, dir = FALSE) g1 <- make_star(10, mode = "undirected") centr_eigen(g0)$centralization centr_eigen(g1)$centralization
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