These functions return logical vectors the length of the ties in a network identifying which hold certain properties or positions in the network.
tie_is_triangular() marks ties that are part of triangles.
tie_is_cyclical() marks ties that are part of cycles.
tie_is_triplet() marks ties that are part of transitive triplets.
tie_is_simmelian() marks ties that are both in a triangle
and fully reciprocated.
tie_is_imbalanced() marks ties that are part of imbalanced triads.
tie_is_transitive() marks ties that complete transitive closure.
They are most useful in highlighting parts of the network that are cohesively connected.
tie_is_triangular(.data)
tie_is_transitive(.data)
tie_is_triplet(.data)
tie_is_cyclical(.data)
tie_is_simmelian(.data)
tie_is_imbalanced(.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_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.
Other marks:
mark_core,
mark_degree,
mark_diff,
mark_dyads,
mark_nodes,
mark_select_node,
mark_select_tie,
mark_ties
Other tie:
mark_dyads,
mark_select_tie,
mark_ties,
measure_broker_tie,
measure_central_tie_between,
measure_central_tie_close,
measure_central_tie_degree,
measure_central_tie_eigen
Other cohesion:
measure_breadth,
measure_cohesion,
measure_fragmentation,
motif_net,
motif_node
ison_monks |> to_uniplex("like") |>
mutate_ties(tri = tie_is_triangular())
#>
#> ── # Sampson's Monks ───────────────────────────────────────────────────────────
#> # A longitudinal, labelled, weighted, directed network of 18 nodes and 168 like
#> arcs over 3 waves
#> # Transformed by exclusion: layers other than 'like' (295 ties excluded)
#>
#> ── Nodes
#> # A tibble: 18 × 3
#> label groups left
#> <chr> <chr> <dbl>
#> 1 Romuald Interstitial 3
#> 2 Bonaventure Loyal 4
#> 3 Ambrose Loyal 4
#> 4 Berthold Loyal 4
#> 5 Peter Loyal 3
#> 6 Louis Loyal 4
#> # ℹ 12 more rows
#>
#> ── Ties
#> # A tibble: 168 × 5
#> from to weight time tri
#> <int> <int> <dbl> <dbl> <tie_mark>
#> 1 1 2 1 2 TRUE
#> 2 1 2 1 3 TRUE
#> 3 1 3 1 3 TRUE
#> 4 1 5 3 1 TRUE
#> 5 1 5 3 2 TRUE
#> 6 1 5 3 3 TRUE
#> # ℹ 162 more rows
#>
ison_adolescents |> to_directed() |>
mutate_ties(trans = tie_is_transitive())
#> IGRAPH 6459a0b DN-- 8 10 -- The Adolescent Society
#> + attr: name (g/c), doi (g/c), year (g/n), vertex1 (g/c), vertex1.total
#> | (g/n), edge.pos (g/c), directed (g/l), name (v/c), trans (e/l)
#> + edges from 6459a0b (vertex names):
#> [1] Betty->Sue Sue ->Alice Alice->Jane Sue ->Dale Dale ->Alice
#> [6] Dale ->Jane Sue ->Pam Pam ->Alice Pam ->Carol Carol->Tina
ison_adolescents |> to_directed() |>
mutate_ties(trip = tie_is_triplet())
#> IGRAPH 3ec574f DN-- 8 10 -- The Adolescent Society
#> + attr: name (g/c), doi (g/c), year (g/n), vertex1 (g/c), vertex1.total
#> | (g/n), edge.pos (g/c), directed (g/l), name (v/c), trip (e/l)
#> + edges from 3ec574f (vertex names):
#> [1] Betty->Sue Alice->Sue Alice->Jane Dale ->Sue Dale ->Alice
#> [6] Jane ->Dale Pam ->Sue Pam ->Alice Pam ->Carol Carol->Tina
ison_adolescents |> to_directed() |>
mutate_ties(cyc = tie_is_cyclical())
#> IGRAPH f8c0c38 DN-- 8 10 -- The Adolescent Society
#> + attr: name (g/c), doi (g/c), year (g/n), vertex1 (g/c), vertex1.total
#> | (g/n), edge.pos (g/c), directed (g/l), name (v/c), cyc (e/l)
#> + edges from f8c0c38 (vertex names):
#> [1] Betty->Sue Sue ->Alice Alice->Jane Dale ->Sue Dale ->Alice
#> [6] Dale ->Jane Pam ->Sue Pam ->Alice Pam ->Carol Carol->Tina
ison_monks |> to_uniplex("like") |>
mutate_ties(simmel = tie_is_simmelian())
#>
#> ── # Sampson's Monks ───────────────────────────────────────────────────────────
#> # A longitudinal, labelled, weighted, directed network of 18 nodes and 168 like
#> arcs over 3 waves
#> # Transformed by exclusion: layers other than 'like' (295 ties excluded)
#>
#> ── Nodes
#> # A tibble: 18 × 3
#> label groups left
#> <chr> <chr> <dbl>
#> 1 Romuald Interstitial 3
#> 2 Bonaventure Loyal 4
#> 3 Ambrose Loyal 4
#> 4 Berthold Loyal 4
#> 5 Peter Loyal 3
#> 6 Louis Loyal 4
#> # ℹ 12 more rows
#>
#> ── Ties
#> # A tibble: 168 × 5
#> from to weight time simmel
#> <int> <int> <dbl> <dbl> <tie_mark>
#> 1 1 2 1 2 FALSE
#> 2 1 2 1 3 FALSE
#> 3 1 3 1 3 FALSE
#> 4 1 5 3 1 FALSE
#> 5 1 5 3 2 FALSE
#> 6 1 5 3 3 FALSE
#> # ℹ 162 more rows
#>
fict_marvel |> to_uniplex("relationship") |> tie_is_imbalanced()
#> `Abomination-Abomination` `Abomination-Beast` `Abomination-Colossus`
#> 1 TRUE FALSE FALSE
#> # ... and 555 more values from this nodeset. Use `print_all(...)` to print all values.