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.
These marks ask only whether a two-path exists, as a census does, so a tie counts however it is signed. Where the network is signed, each tie is therefore read by its magnitude, and every tie keeps its place in the returned vector.
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,
member_community_link
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 8bc6d0d 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 8bc6d0d (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 d029212 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 d029212 (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 4b8f9d4 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 4b8f9d4 (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.