Marks of ties based on dyadic properties

These functions return logical vectors the length of the ties in a network identifying which are embedded within particular dyads.

• tie_is_multiple() marks ties that repeat an earlier tie between the same two nodes.

• tie_is_reciprocated() marks ties that are mutual/reciprocated.

They are most useful in highlighting parts of the network where relationships are denser.

tie_is_multiple(.data)

tie_is_reciprocated(.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_mark logical vector the length of the ties in the network, giving either TRUE or FALSE for each tie depending on whether the condition is matched.

Multiple and parallel ties

tie_is_multiple() and manynet::tie_is_parallel() answer different questions, and can give different answers on the same network.

tie_is_multiple() marks only the repeats, as igraph::which_multiple() does: the first tie between two nodes is FALSE, and each further tie between them is TRUE. It reads nothing but the two nodes a tie joins, so ties at different times, in different layers, or from different reporters all count as repeats of each other.

manynet::tie_is_parallel() marks every tie in such a bundle, the first included, but only where the ties coexist: at the same time or over overlapping intervals, in the same layer, and from the same reporter. Use it to ask whether a network holds ties that could be combined.

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 marks: mark_core, mark_degree, mark_diff, mark_nodes, mark_select_node, mark_select_tie, mark_ties, mark_triangles

Other tie: mark_select_tie, mark_ties, mark_triangles, measure_broker_tie, measure_central_tie_between, measure_central_tie_close, measure_central_tie_degree, measure_central_tie_eigen, member_community_link

Examples

tie_is_multiple(fict_marvel)
#>   `Abomination-Abomination` `Abomination-Beast` `Abomination-Colossus`
#> 1 FALSE                     FALSE               FALSE                 
#> # ... and 1238 more values from this nodeset. Use `print_all(...)` to print all values.
tie_is_reciprocated(ison_algebra)
#>   `1->5` `1->5` `1->8` `1->9` `1->9` `1->10` `1->10` `1->11` `1->12` `1->12`
#> 1 TRUE   TRUE   FALSE  TRUE   TRUE   TRUE    TRUE    TRUE    TRUE    TRUE   
#> # ... and 269 more values from this nodeset. Use `print_all(...)` to print all values.