measure_hierarchy {manynet}

Graph theoretic dimensions of hierarchy

Description

These functions, together with net_reciprocity(), are used jointly to measure how hierarchical a network is:

• net_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.

• net_efficiency() measures the Krackhardt efficiency score.

• net_upperbound() measures the Krackhardt (least) upper bound score.

Usage

net_connectedness(.data)

net_efficiency(.data)

net_upperbound(.data)

Arguments

.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

References

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

See Also

Other measures: between_centrality, close_centrality, degree_centrality, eigenv_centrality, measure_attributes, measure_closure, measure_cohesion, measure_features, measure_heterogeneity, measure_holes, measure_infection, measure_net_diffusion, measure_node_diffusion, measure_periods, measure_properties, member_diffusion

Examples

net_connectedness(ison_networkers)
1 - net_reciprocity(ison_networkers)
net_efficiency(ison_networkers)
net_upperbound(ison_networkers)