Measures of network centralisation
An object of a migraph-consistent class:
matrix (adjacency or incidence) from {base}
R
edgelist, a data frame from {base}
R or tibble from {tibble}
igraph, from the {igraph}
package
network, from the {network}
package
tbl_graph, from the {tidygraph}
package
Logical scalar, whether the centrality scores are normalized. Different denominators are used depending on whether the object is one-mode or two-mode, the type of centrality, and other arguments.
Character string, “out” bases the measure on outgoing ties, “in” on incoming ties, and "all" on either/the sum of the two. For two-mode networks, "all" uses as numerator the sum of differences between the maximum centrality score for the mode against all other centrality scores in the network, whereas "in" uses as numerator the sum of differences between the maximum centrality score for the mode against only the centrality scores of the other nodes in that mode.
network_degree()
: Calculate the degree centralization for a graph
network_closeness()
: Calculate the closeness centralization for a graph
network_betweenness()
: Calculate the betweenness centralization for a graph
network_eigenvector()
: Calculate the eigenvector centralization for a graph
Other measures:
centrality
,
closure
,
cohesion()
,
diversity
,
features
,
holes
,
tie_centrality
network_degree(ison_southern_women, direction = "in")
#> Mode 1 Mode 2
#> 0.231 0.466
network_closeness(ison_southern_women, direction = "in")
#> Mode 1 Mode 2
#> 0.214 0.528
network_betweenness(ison_southern_women, direction = "in")
#> Mode 1 Mode 2
#> 0.0668 0.1982
network_eigenvector(mpn_elite_mex)
#> [1] 0.63
network_eigenvector(ison_southern_women)
#> Mode 1 Mode 2
#> 0.0849 0.2630