Measuring ties eigenvector-like centrality

tie_by_eigenvector() measures the eigenvector centrality of ties in 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.

tie_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 tie_measure numeric vector the length of the ties in the network, providing the scores for each tie. If the network is labelled, then the scores will be labelled with the ties' adjacent nodes' names.

See also

Other eigenvector: measure_central_eigen, measure_centralisation_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_centralisation_eigen, measure_centralities_between, measure_centralities_close, measure_centralities_degree

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_centralisation_eigen, measure_centralities_between, measure_centralities_close, measure_centralities_degree, 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

Other tie: mark_dyads, mark_select_tie, mark_ties, mark_triangles, measure_broker_tie, measure_centralities_between, measure_centralities_close, measure_centralities_degree

Examples

tie_by_eigenvector(ison_adolescents)
#>   `Betty-Sue` `Sue-Alice` `Alice-Jane` `Sue-Dale` `Alice-Dale` `Jane-Dale`
#> 1       0.574           1        0.701      0.821        0.849       0.521
#> # ... and 4 more values from this nodeset. Use `print_all(...)` to print all values.