close_centrality  R Documentation 
These functions calculate common closenessrelated centrality measures for one and twomode networks:
node_closeness()
measures the closeness centrality of nodes in a network.
node_reach()
measures nodes' reach centrality,
or how many nodes they can reach within k steps.
node_harmonic()
measures nodes' harmonic centrality or valued centrality,
which is thought to behave better than reach centrality for disconnected networks.
node_information()
measures nodes' information centrality or
currentflow closeness centrality.
tie_closeness()
measures the closeness of each tie to other ties in the network.
network_closeness()
measures a network's closeness centralization.
network_reach()
measures a network's reach centralization.
network_harmonic()
measures a network's harmonic centralization.
All measures attempt to use as much information as they are offered,
including whether the networks are directed, weighted, or multimodal.
If this would produce unintended results,
first transform the salient properties using e.g. to_undirected()
functions.
All centrality and centralization measures return normalized measures by default,
including for twomode networks.
node_closeness(.data, normalized = TRUE, direction = "out", cutoff = NULL)
node_reach(.data, normalized = TRUE, k = 2)
node_harmonic(.data, normalized = TRUE, k = 1)
node_information(.data, normalized = TRUE)
tie_closeness(.data, normalized = TRUE)
network_closeness(.data, normalized = TRUE, direction = c("all", "out", "in"))
network_reach(.data, normalized = TRUE, k = 2)
network_harmonic(.data, normalized = TRUE, k = 2)
.data 
An object of a

normalized 
Logical scalar, whether the centrality scores are normalized. Different denominators are used depending on whether the object is onemode or twomode, the type of centrality, and other arguments. 
direction 
Character string, “out” bases the measure on outgoing ties, “in” on incoming ties, and "all" on either/the sum of the two. For twomode networks, "all" uses as numerator the sum of differences between the maximum centrality score for the mode against all other centrality scores in the network, whereas "in" uses as numerator the sum of differences between the maximum centrality score for the mode against only the centrality scores of the other nodes in that mode. 
cutoff 
Maximum path length to use during calculations. 
k 
Integer of steps out to calculate reach 
Marchiori, M, and V Latora. 2000. "Harmony in the smallworld". Physica A 285: 539546.
Dekker, Anthony. 2005. "Conceptual distance in social network analysis". Journal of Social Structure 6(3).
Other centrality:
between_centrality
,
degree_centrality
,
eigenv_centrality
Other measures:
between_centrality
,
closure
,
cohesion()
,
degree_centrality
,
eigenv_centrality
,
features
,
heterogeneity
,
hierarchy
,
holes
,
net_diffusion
,
node_diffusion
,
periods
node_closeness(mpn_elite_mex)
node_closeness(ison_southern_women)
node_reach(ison_adolescents)
(ec < tie_closeness(ison_adolescents))
plot(ec)
#ison_adolescents %>%
# activate(edges) %>% mutate(weight = ec) %>%
# autographr()
network_closeness(ison_southern_women, direction = "in")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.