tie_by_cohesion() measures the ratio between common neighbors to ties'
adjacent nodes and the total number of adjacent nodes,
where high values indicate ties' embeddedness in dense local environments.
A tie whose two endpoints have no other neighbours has nothing to be
embedded in, and so returns NaN rather than 0.
tie_by_cohesion(.data)A network object of class stocnet, igraph, tbl_graph, network, or similar.
Internally any of these will be coerced to an efficient implementation.
For more information on possible coercions, see e.g. manynet::as_stocnet().
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.
The object also carries the measure it computed, the range its values
can fall within, and whether and how those values were normalized.
These are shown as a one-line header when the object is printed.
Where a measure offers a choice between several ways of counting the
same thing, it also carries the variant it used.
All can be retrieved with attr().
Other brokerage:
measure_broker_node,
measure_brokerage,
member_brokerage,
motif_brokerage_net,
motif_brokerage_node
Other measures:
measure_assort_net,
measure_assort_node,
measure_breadth,
measure_broker_node,
measure_brokerage,
measure_central_between,
measure_central_close,
measure_central_degree,
measure_central_eigen,
measure_central_tie_between,
measure_central_tie_close,
measure_central_tie_degree,
measure_central_tie_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_fit,
measure_fragmentation,
measure_hierarchy,
measure_periods
Other tie:
mark_dyads,
mark_select_tie,
mark_ties,
mark_triangles,
measure_central_tie_between,
measure_central_tie_close,
measure_central_tie_degree,
measure_central_tie_eigen
tie_by_cohesion(ison_adolescents)
#> # Cohesion, normalized [0, 1]
#> `Betty-Sue` `Sue-Alice` `Alice-Jane` `Sue-Dale` `Alice-Dale` `Jane-Dale`
#> 1 0 0.5 0.333 0.25 0.667 0.5
#> # ... and 4 more values from this nodeset. Use `print_all(...)` to print all values.