These functions offer methods for summarising the closure in configurations in one-, two-, and three-mode networks.

network_reciprocity(object, method = "default")

node_reciprocity(object)

network_transitivity(object)

node_transitivity(object)

network_equivalency(object)

network_congruency(object, object2)

Arguments

object

A one-mode or two-mode matrix, igraph, or tidygraph

method

For reciprocity, either default or ratio. See ?igraph::reciprocity

object2

Optionally, a second (two-mode) matrix, igraph, or tidygraph

Details

For one-mode networks, shallow wrappers of igraph versions exist via network_reciprocity and network_transitivity.

For two-mode networks, network_equivalency calculates the proportion of three-paths in the network that are closed by fourth tie to establish a "shared four-cycle" structure.

For three-mode networks, network_congruency calculates the proportion of three-paths spanning two two-mode networks that are closed by a fourth tie to establish a "congruent four-cycle" structure.

Functions

  • network_reciprocity(): Calculate reciprocity in a (usually directed) network

  • node_reciprocity(): Calculate nodes' reciprocity

  • network_transitivity(): Calculate transitivity in a network

  • node_transitivity(): Calculate nodes' transitivity

  • network_equivalency(): Calculate equivalence or reinforcement in a (usually two-mode) network

  • network_congruency(): Calculate congruency across two two-mode networks

References

Robins, Garry L, and Malcolm Alexander. 2004. Small worlds among interlocking directors: Network structure and distance in bipartite graphs. Computational & Mathematical Organization Theory 10(1): 69–94. doi:10.1023/B:CMOT.0000032580.12184.c0 .

Knoke, David, Mario Diani, James Hollway, and Dimitris C Christopoulos. 2021. Multimodal Political Networks. Cambridge University Press. Cambridge University Press. doi:10.1017/9781108985000

Examples

network_reciprocity(ison_southern_women)
#> [1] 1
node_reciprocity(to_unweighted(ison_networkers))
#>   `LIN FREEMAN` DOUG W…¹ EV RO…² RICHA…³ PHIPP…⁴ CAROL…⁵ GARY …⁶ RUSS …⁷ JOHN …⁸
#> 1         0.935     0.75       1   0.944   0.286     0.8       1     0.8   0.818
#> # ... with 23 more from this nodeset in the vector.
network_transitivity(ison_adolescents)
#> [1] 0.45
node_transitivity(ison_adolescents)
#>      V1    V2    V3    V4    V5    V6    V7    V8
#> 1   NaN 0.333   0.5     1 0.667 0.333     0   NaN
network_equivalency(ison_southern_women)
#> [1] 0.487