Measuring networks eigenvector-like centralisation

net_by_eigenvector() measures the eigenvector centralization for a network.

All measures attempt to use as much information as they are offered, including whether the networks are directed, weighted, or multimodal. If this would produce unintended results, first transform the salient properties using e.g. manynet::to_undirected() functions. All centrality and centralization measures return normalized measures by default, including for two-mode networks.

net_by_eigenvector(.data, normalized = TRUE)

Arguments

.data

A network object of class mnet, igraph, tbl_graph, network, or similar. For more information on the standard coercion possible, see manynet::as_tidygraph().

normalized

Logical scalar, whether scores are normalized. Different denominators may be used depending on the measure, whether the object is one-mode or two-mode, and other arguments. By default TRUE.

Value

A network_measure numeric score.

See also

Other eigenvector: measure_central_eigen, measure_centralities_eigen

Other centrality: measure_central_between, measure_central_close, measure_central_degree, measure_central_eigen, measure_centralisation_between, measure_centralisation_close, measure_centralisation_degree, measure_centralities_between, measure_centralities_close, measure_centralities_degree, measure_centralities_eigen

Other measures: measure_assort_net, measure_assort_node, measure_breadth, measure_broker_node, measure_broker_tie, measure_brokerage, measure_central_between, measure_central_close, measure_central_degree, measure_central_eigen, measure_centralisation_between, measure_centralisation_close, measure_centralisation_degree, measure_centralities_between, measure_centralities_close, measure_centralities_degree, measure_centralities_eigen, measure_closure, measure_closure_node, measure_cohesion, measure_core, measure_diffusion_infection, measure_diffusion_net, measure_diffusion_node, measure_diverse_net, measure_diverse_node, measure_features, measure_fragmentation, measure_hierarchy, measure_periods

Examples

net_by_eigenvector(ison_southern_women)
#> Mode 1 Mode 2 
#> 0.0849 0.2630