These functions include ways to measure nodes' brokerage activity and exclusivity in a network:
node_by_brokering_activity() measures nodes' brokerage activity.
node_by_brokering_exclusivity() measures nodes' brokerage exclusivity.
node_by_brokering_activity(.data, membership)
node_by_brokering_exclusivity(.data, membership)A network object of class stocnet, igraph, tbl_graph, network, or similar.
Internally any of these will be coerced to an efficient implementation.
For more information on possible coercions, see e.g. manynet::as_stocnet().
A character string naming an existing node attribute in
the network, or a categorical vector of the same length as the number of
nodes in the network where each element indicates the group membership of
the corresponding node.
While this may often be a vector created using node_in_*() functions,
it can be any character vector that assigns nodes to groups or categories.
A node_measure numeric vector the length of the nodes in the network,
providing the scores for each node.
If the network is labelled,
then the scores will be labelled with the nodes' names.
The object also carries the measure it computed, the range its values
can fall within, and whether and how those values were normalized.
These are shown as a one-line header when the object is printed.
Where a measure offers a choice between several ways of counting the
same thing, it also carries the variant it used.
All can be retrieved with attr().
Hamilton, Matthew, Jacob Hileman, and Orjan Bodin. 2020. "Evaluating heterogeneous brokerage: New conceptual and methodological approaches and their application to multi-level environmental governance networks" Social Networks 61: 1-10. doi:10.1016/j.socnet.2019.08.002
Other measures:
measure_assort_net,
measure_assort_node,
measure_breadth,
measure_broker_node,
measure_broker_tie,
measure_central_between,
measure_central_close,
measure_central_degree,
measure_central_eigen,
measure_central_tie_between,
measure_central_tie_close,
measure_central_tie_degree,
measure_central_tie_eigen,
measure_closure,
measure_closure_node,
measure_cohesion,
measure_core,
measure_diffusion_infection,
measure_diffusion_net,
measure_diffusion_node,
measure_diverse_net,
measure_diverse_node,
measure_features,
measure_fit,
measure_fragmentation,
measure_hierarchy,
measure_periods
Other nodal:
mark_core,
mark_degree,
mark_diff,
mark_nodes,
mark_select_node,
measure_assort_node,
measure_broker_node,
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_clique,
motif_composition,
motif_exposure,
motif_node,
motif_path
Other brokerage:
measure_broker_node,
measure_broker_tie,
member_brokerage,
motif_brokerage_net,
motif_brokerage_node
node_by_brokering_exclusivity(ison_networkers, "Discipline")
#> # Brokerage exclusivity [0, Inf)
#> █▁▁▁▁▁
#> `Lin Freeman` `Doug White` `Ev Rogers` `Richard Alba` `Phipps Arabie`
#> 1 1 0 0 0 0
#> # ... and 27 more values from this nodeset. Use `print_all(...)` to print all values.