These functions, together with network_reciprocity(), are used jointly to
measure how hierarchical a network is:
network_connectedness() measures the proportion of dyads in the network
that are reachable to one another,
or the degree to which network is a single component.
network_efficiency() measures the Krackhardt efficiency score.
network_upperbound() measures the Krackhardt (least) upper bound score.
network_connectedness(.data)
network_efficiency(.data)
network_upperbound(.data)An object of a {manynet}-consistent class:
matrix (adjacency or incidence) from {base} R
edgelist, a data frame from {base} R or tibble from {tibble}
igraph, from the {igraph} package
network, from the {network} package
tbl_graph, from the {tidygraph} package
Krackhardt, David. 1994. Graph theoretical dimensions of informal organizations. In Carley and Prietula (eds) Computational Organizational Theory, Hillsdale, NJ: Lawrence Erlbaum Associates. Pp. 89-111.
Everett, Martin, and David Krackhardt. 2012. “A second look at Krackhardt's graph theoretical dimensions of informal organizations.” Social Networks, 34: 159-163. doi:10.1016/j.socnet.2011.10.006
Other measures:
between_centrality,
close_centrality,
closure,
cohesion(),
degree_centrality,
eigenv_centrality,
features,
heterogeneity,
holes,
net_diffusion,
node_diffusion,
periods
network_connectedness(ison_networkers)
#> [1] 1
1 - network_reciprocity(ison_networkers)
#> [1] 0.209
network_efficiency(ison_networkers)
#> [1] 0.0705
network_upperbound(ison_networkers)
#> [1] 1