Measures of tie brokerage

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)

Arguments

.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().

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.

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().

Cognitive social structures

A cognitive social structure records each node's report of the ties in the whole network, in a by column that names who reported each tie. Counting every report as a tie of its own would count each tie once for every perceiver who reports it. So the functions here first combine the reports into the locally aggregated structure of Krackhardt (1987), with the intersection rule: a tie exists if both of its ends report it, and a message says so. A tie that names no reporter is kept as it is.

A tie-level function still returns one value for each report, so that the result can be added back to the network it was given. Each report takes the value of the tie that it reports. A report of a tie that is not in the aggregated structure takes NA, or FALSE for a mark. tie_is_random() is the exception, and draws among the reports.

To combine the reports in a different way, do this before the function, e.g. with manynet::to_aggregated(over = "by").

Krackhardt, David. 1987. "Cognitive social structures". Social Networks 9(2): 109-134. doi:10.1016/0378-8733(87)90009-8

See also

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, member_community_link

Examples

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.