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 in triangles.
tie_is_cyclical() marks ties that are in cycles.
tie_is_transitive() marks ties that complete transitive closure.
tie_is_triplet() marks ties that are in a transitive triplet.
tie_is_simmelian() marks ties that are both in a triangle
and fully reciprocated.
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_forbidden(.data)
tie_is_imbalanced(.data)Other marks:
mark_diff,
mark_nodes,
mark_select,
mark_tie_select,
mark_ties
ison_monks %>% to_uniplex("like") %>%
mutate_ties(tri = tie_is_triangular())
#>
#> ── # Sampson's Monks ───────────────────────────────────────────────────────────
#> # A longitudinal, labelled, multiplex, weighted, directed network of 18 nodes
#> and 168 arcs over 3 waves
#>
#> ── Nodes
#> # A tibble: 18 × 3
#> name 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 wave 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
#>
#graphr(edge_color = "tri")
ison_adolescents %>% to_directed() %>%
mutate_ties(trans = tie_is_transitive())
#> ── # The Adolescent Society ────────────────────────────────────────────────────
#> # A labelled, multiplex, directed network of 8 adolescents and 10 friendships
#> arcs
#>
#> ── Nodes
#> # A tibble: 8 × 1
#> name
#> <chr>
#> 1 Betty
#> 2 Sue
#> 3 Alice
#> 4 Jane
#> 5 Dale
#> 6 Pam
#> # ℹ 2 more rows
#>
#> ── Ties
#> # A tibble: 10 × 3
#> from to trans
#> <int> <int> <tie_mark>
#> 1 2 1 FALSE
#> 2 3 2 FALSE
#> 3 3 4 FALSE
#> 4 2 5 FALSE
#> 5 5 3 FALSE
#> 6 5 4 TRUE
#> # ℹ 4 more rows
#>
#graphr(edge_color = "trans")
ison_adolescents %>% to_directed() %>%
mutate_ties(trip = tie_is_triplet())
#> ── # The Adolescent Society ────────────────────────────────────────────────────
#> # A labelled, multiplex, directed network of 8 adolescents and 10 friendships
#> arcs
#>
#> ── Nodes
#> # A tibble: 8 × 1
#> name
#> <chr>
#> 1 Betty
#> 2 Sue
#> 3 Alice
#> 4 Jane
#> 5 Dale
#> 6 Pam
#> # ℹ 2 more rows
#>
#> ── Ties
#> # A tibble: 10 × 3
#> from to trip
#> <int> <int> <tie_mark>
#> 1 2 1 FALSE
#> 2 2 3 TRUE
#> 3 4 3 TRUE
#> 4 5 2 FALSE
#> 5 3 5 TRUE
#> 6 4 5 TRUE
#> # ℹ 4 more rows
#>
#graphr(edge_color = "trip")
ison_adolescents %>% to_directed() %>%
mutate_ties(cyc = tie_is_cyclical())
#> ── # The Adolescent Society ────────────────────────────────────────────────────
#> # A labelled, multiplex, directed network of 8 adolescents and 10 friendships
#> arcs
#>
#> ── Nodes
#> # A tibble: 8 × 1
#> name
#> <chr>
#> 1 Betty
#> 2 Sue
#> 3 Alice
#> 4 Jane
#> 5 Dale
#> 6 Pam
#> # ℹ 2 more rows
#>
#> ── Ties
#> # A tibble: 10 × 3
#> from to cyc
#> <int> <int> <tie_mark>
#> 1 2 1 FALSE
#> 2 2 3 TRUE
#> 3 4 3 FALSE
#> 4 5 2 FALSE
#> 5 5 3 FALSE
#> 6 4 5 FALSE
#> # ℹ 4 more rows
#>
#graphr(edge_color = "cyc")
ison_monks %>% to_uniplex("like") %>%
mutate_ties(simmel = tie_is_simmelian())
#> ── # Sampson's Monks ───────────────────────────────────────────────────────────
#> # A longitudinal, labelled, multiplex, weighted, directed network of 18 nodes
#> and 168 arcs over 3 waves
#>
#> ── Nodes
#> # A tibble: 18 × 3
#> name 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 wave 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
#>
#graphr(edge_color = "simmel")
generate_random(8, directed = TRUE) %>%
mutate_ties(forbid = tie_is_forbidden())
#> ── # Erdos-Renyi (gnp) graph ───────────────────────────────────────────────────
#> # A multiplex, directed network of 8 nodes and 29 arcs
#>
#> ── Ties
#> # A tibble: 29 × 3
#> from to forbid
#> <int> <int> <tie_mark>
#> 1 2 1 TRUE
#> 2 5 1 FALSE
#> 3 7 1 TRUE
#> 4 1 2 TRUE
#> 5 3 2 FALSE
#> 6 4 2 TRUE
#> # ℹ 23 more rows
#>
#graphr(edge_color = "forbid")
tie_is_imbalanced(ison_marvel_relationships)
#> `Abomination-Beast` `Abomination-Colossus` `Abomination-Cyclops`
#> 1 TRUE TRUE TRUE
#> # ... with 555 more values from this nodeset unprinted. Use `print(..., n = Inf)` to print all values.