These functions include ways to take a census of the positions of nodes in a network:
node_by_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_by_triad() returns a census of the triad configurations
nodes are embedded in.
node_by_quad() returns a census of nodes' positions
in motifs of four nodes.
node_by_path() returns the shortest path lengths
of each node to every other node in the network.
node_by_tie(.data)
node_by_dyad(.data)
node_by_triad(.data)
node_by_quad(.data)
node_by_path(.data)Holland, Paul W., and Samuel Leinhardt. 1970. "A Method for Detecting Structure in Sociometric Data". American Journal of Sociology, 76: 492-513. doi:10.1016/B978-0-12-442450-0.50028-6
Davis, James A., and Samuel Leinhardt. 1967. “The Structure of Positive Interpersonal Relations in Small Groups.” 55.
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,
motif_diffusion,
motif_net
task_eg <- to_named(to_uniplex(ison_algebra, "tasks"))
(tie_cen <- node_by_tie(task_eg))
#> # A tibble: 16 × 33
#> names fromAda fromBeulah fromCaleb fromDalton fromEileen fromFannie fromGerald
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Ada 0 0 0 0 0.3 0 0
#> 2 Beul… 0 0 0 0 0 0 1.2
#> 3 Caleb 0 0 0 0 0.15 0.75 0
#> 4 Dalt… 0 0 0 0 0 0 0
#> 5 Eile… 0.3 0 0.15 0 0 0.45 0
#> 6 Fann… 0 0 0.6 0 0.75 0 0
#> # ℹ 10 more rows
#> # ℹ 25 more variables: fromHeather <dbl>, fromIva <dbl>, fromJean <dbl>,
#> # fromKaiden <dbl>, fromLulu <dbl>, fromMarcia <dbl>, fromNaomi <dbl>,
#> # fromOla <dbl>, fromPhilip <dbl>, toAda <dbl>, toBeulah <dbl>,
#> # toCaleb <dbl>, toDalton <dbl>, toEileen <dbl>, toFannie <dbl>,
#> # toGerald <dbl>, toHeather <dbl>, toIva <dbl>, toJean <dbl>, toKaiden <dbl>,
#> # toLulu <dbl>, toMarcia <dbl>, toNaomi <dbl>, toOla <dbl>, toPhilip <dbl>
node_by_dyad(ison_networkers)
#> # A tibble: 32 × 4
#> names Mutual Asymmetric Null
#> <chr> <dbl> <dbl> <dbl>
#> 1 Lin Freeman 29 2 1
#> 2 Doug White 21 10 1
#> 3 Ev Rogers 3 8 21
#> 4 Richard Alba 17 2 13
#> 5 Phipps Arabie 8 20 4
#> 6 Carol Barner-Barry 12 4 16
#> # ℹ 26 more rows
(triad_cen <- node_by_triad(task_eg))
#> # A tibble: 16 × 17
#> names X003 X012 X102 X021D X021U X021C X111D X111U X030T X030C X201 X120D
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Ada 23 11 39 0 0 0 2 2 0 0 17 0
#> 2 Beulah 21 6 47 0 0 0 5 6 0 0 10 0
#> 3 Caleb 34 3 46 0 0 0 2 1 0 0 13 0
#> 4 Dalton 52 6 28 0 0 0 0 0 0 0 18 0
#> 5 Eileen 17 11 37 1 0 1 6 7 0 0 15 1
#> 6 Fannie 23 8 40 1 0 0 3 3 0 0 18 1
#> # ℹ 10 more rows
#> # ℹ 4 more variables: X120U <dbl>, X120C <dbl>, X210 <dbl>, X300 <dbl>
node_by_quad(ison_southern_women)
#> # A tibble: 18 × 12
#> names E4 I4 H4 L4 D4 U4 Y4 P4 C4 Z4 X4
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Evelyn 1241 1347 176 1138 272 0 246 0 75 0 0
#> 2 Laura 1365 1389 180 1077 213 0 203 0 68 0 0
#> 3 Theresa 1333 1244 123 1137 286 0 281 0 91 0 0
#> 4 Brenda 1384 1356 176 1093 210 0 205 0 71 0 0
#> 5 Charlotte 1728 1562 157 836 117 0 74 0 21 0 0
#> 6 Frances 1840 1433 114 810 134 0 134 0 30 0 0
#> # ℹ 12 more rows
#> # A tibble: 14 × 12
#> names E4 I4 H4 L4 D4 U4 Y4 P4 C4 Z4 X4
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 E1 1877 1632 96 710 113 0 52 0 15 0 0
#> 2 E2 1886 1631 84 698 122 0 58 0 16 0 0
#> 3 E3 1368 1611 190 1006 168 0 98 0 54 0 0
#> 4 E4 1681 1673 131 784 138 0 64 0 24 0 0
#> 5 E5 1061 1557 232 1208 216 0 140 0 81 0 0
#> 6 E6 1085 1576 178 1142 287 0 156 0 71 0 0
#> # ℹ 8 more rows
node_by_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_by_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>