These functions are a collection of node measures that do not really fall
into the class of centrality measures. For lack of a better place they are
collected under the node_* umbrella of functions.
node_eccentricity(mode = "out")node_constraint(weights = NULL)
node_coreness(mode = "out")
node_diversity(weights)
node_efficiency(weights = NULL, directed = TRUE, mode = "all")
node_bridging_score()
node_effective_network_size()
node_connectivity_impact()
node_closeness_impact()
node_fareness_impact()
A numeric vector of the same length as the number of nodes in the graph.
How edges are treated. In node_coreness() it chooses which kind
of coreness measure to calculate. In node_efficiency() it defines how the
local neighborhood is created
The weights to use for each node during calculation
Should the graph be treated as a directed graph if it is in fact directed
node_eccentricity(): measure the maximum shortest path to all other nodes in the graph
node_constraint(): measures Burts constraint of the node. See igraph::constraint()
node_coreness(): measures the coreness of each node. See igraph::coreness()
node_diversity(): measures the diversity of the node. See igraph::diversity()
node_efficiency(): measures the local efficiency around each node. See igraph::local_efficiency()
node_bridging_score(): measures Valente's Bridging measures for detecting structural bridges (influenceR)
node_effective_network_size(): measures Burt's Effective Network Size indicating access to structural holes in the network (influenceR)
node_connectivity_impact(): measures the impact on connectivity when removing the node (NetSwan)
node_closeness_impact(): measures the impact on closeness when removing the node (NetSwan)
node_fareness_impact(): measures the impact on fareness (distance between all node pairs) when removing the node (NetSwan)
# Calculate Burt's Constraint for each node
create_notable('meredith') %>%
mutate(b_constraint = node_constraint())
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