These functions include ways to take a census of the positions of nodes in a network:
node_x_tie() returns a census of the ties in a network.
For directed networks, out-ties and in-ties are bound together.
For multiplex networks, the various types of ties are bound together.
node_x_path() returns the shortest path lengths
of each node to every other node in the network.
node_x_tie(.data)
node_x_path(.data)A network object of class mnet, igraph, tbl_graph, network, or similar.
For more information on the standard coercion possible,
see manynet::as_tidygraph().
A node_motif matrix with one row for each node in the network and
a column for each motif type,
giving the count of each motif in which each node participates.
It is printed as a tibble, however, to avoid greedy printing.
If the network is labelled,
then the node names will be in a column named names.
Dijkstra, Edsger W. 1959. "A note on two problems in connexion with graphs". Numerische Mathematik 1, 269-71. doi:10.1007/BF01386390 .
Opsahl, Tore, Filip Agneessens, and John Skvoretz. 2010. "Node centrality in weighted networks: Generalizing degree and shortest paths". Social Networks 32(3): 245-51. doi:10.1016/j.socnet.2010.03.006 .
Other motifs:
motif_brokerage_net,
motif_brokerage_node,
motif_exposure,
motif_hazard,
motif_hierarchy,
motif_net,
motif_node,
motif_periods
Other nodal:
mark_core,
mark_degree,
mark_diff,
mark_nodes,
mark_select_node,
measure_assort_node,
measure_broker_node,
measure_brokerage,
measure_central_between,
measure_central_close,
measure_central_degree,
measure_central_eigen,
measure_closure_node,
measure_core,
measure_diffusion_node,
measure_diverse_node,
member_brokerage,
member_cliques,
member_community,
member_community_hier,
member_community_non,
member_components,
member_core,
member_diffusion,
member_equivalence,
motif_brokerage_node,
motif_exposure,
motif_node
task_eg <- to_named(to_uniplex(ison_algebra, "tasks"))
(tie_cen <- node_x_tie(task_eg))
#> # A tibble: 16 × 33
#> names fromAllen fromBeatrice fromCynthia fromDalton fromEliana fromFern
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Allen 0 0 0 0 0.3 0
#> 2 Beatrice 0 0 0 0 0 0
#> 3 Cynthia 0 0 0 0 0.15 0.75
#> 4 Dalton 0 0 0 0 0 0
#> 5 Eliana 0.3 0 0.15 0 0 0.45
#> 6 Fern 0 0 0.6 0 0.75 0
#> # ℹ 10 more rows
#> # ℹ 26 more variables: fromGraham <dbl>, fromHope <dbl>, fromIsabella <dbl>,
#> # fromJerry <dbl>, fromKristopher <dbl>, fromLindsay <dbl>, fromMathew <dbl>,
#> # fromNicolas <dbl>, fromOliver <dbl>, fromPatty <dbl>, toAllen <dbl>,
#> # toBeatrice <dbl>, toCynthia <dbl>, toDalton <dbl>, toEliana <dbl>,
#> # toFern <dbl>, toGraham <dbl>, toHope <dbl>, toIsabella <dbl>,
#> # toJerry <dbl>, toKristopher <dbl>, toLindsay <dbl>, toMathew <dbl>, …
node_x_path(ison_adolescents)
#> # A tibble: 8 × 9
#> names Betty Sue Alice Jane Dale Pam Carol Tina
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Betty 0 1 2 3 2 2 3 4
#> 2 Sue 1 0 1 2 1 1 2 3
#> 3 Alice 2 1 0 1 1 1 2 3
#> 4 Jane 3 2 1 0 1 2 3 4
#> 5 Dale 2 1 1 1 0 2 3 4
#> 6 Pam 2 1 1 2 2 0 1 2
#> # ℹ 2 more rows
node_x_path(ison_southern_women)
#> # A tibble: 18 × 33
#> names Evelyn Laura Theresa Brenda Charlotte Frances Eleanor Pearl Ruth Verne
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Evelyn 0 2 2 2 2 2 2 2 2 2
#> 2 Laura 2 0 2 2 2 2 2 2 2 2
#> 3 There… 2 2 0 2 2 2 2 2 2 2
#> 4 Brenda 2 2 2 0 2 2 2 2 2 2
#> 5 Charl… 2 2 2 2 0 2 2 4 2 2
#> 6 Franc… 2 2 2 2 2 0 2 2 2 2
#> # ℹ 12 more rows
#> # ℹ 22 more variables: Myra <dbl>, Katherine <dbl>, Sylvia <dbl>, Nora <dbl>,
#> # Helen <dbl>, Dorothy <dbl>, Olivia <dbl>, Flora <dbl>, E1 <dbl>, E2 <dbl>,
#> # E3 <dbl>, E4 <dbl>, E5 <dbl>, E6 <dbl>, E7 <dbl>, E8 <dbl>, E9 <dbl>,
#> # E10 <dbl>, E11 <dbl>, E12 <dbl>, E13 <dbl>, E14 <dbl>
#> # A tibble: 14 × 33
#> names Evelyn Laura Theresa Brenda Charlotte Frances Eleanor Pearl Ruth Verne
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 E1 1 1 3 1 3 3 3 3 3 3
#> 2 E2 1 1 1 3 3 3 3 3 3 3
#> 3 E3 1 1 1 1 1 1 3 3 3 3
#> 4 E4 1 3 1 1 1 3 3 3 3 3
#> 5 E5 1 1 1 1 1 1 1 3 1 3
#> 6 E6 1 1 1 1 3 1 1 1 3 3
#> # ℹ 8 more rows
#> # ℹ 22 more variables: Myra <dbl>, Katherine <dbl>, Sylvia <dbl>, Nora <dbl>,
#> # Helen <dbl>, Dorothy <dbl>, Olivia <dbl>, Flora <dbl>, E1 <dbl>, E2 <dbl>,
#> # E3 <dbl>, E4 <dbl>, E5 <dbl>, E6 <dbl>, E7 <dbl>, E8 <dbl>, E9 <dbl>,
#> # E10 <dbl>, E11 <dbl>, E12 <dbl>, E13 <dbl>, E14 <dbl>