Centralize a graph according to the eigenvector centrality of verticesSource:
centralize() for a summary of graph centralization.
centr_eigen( graph, directed = FALSE, scale = TRUE, options = arpack_defaults(), normalized = TRUE )
The input graph.
logical scalar, whether to use directed shortest paths for calculating eigenvector centrality.
Whether to rescale the eigenvector centrality scores, such that the maximum score is one.
This is passed to
eigen_centrality(), the options for the ARPACK eigensolver.
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 corresponding eigenvalue.
ARPACK options, see the return value of
The graph level centrality index.
The same as above, the theoretical maximum centralization score for a graph with the same number of vertices.
# A BA graph is quite centralized g <- sample_pa(1000, m = 4) centr_degree(g)$centralization #>  0.1357814 centr_clo(g, mode = "all")$centralization #>  0.3885529 centr_betw(g, directed = FALSE)$centralization #>  0.2004031 centr_eigen(g, directed = FALSE)$centralization #>  0.9345768 # 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)$centralization #>  1 centr_eigen(g1)$centralization #>  0.75