R/measure_centrality_closeness.R
measure_centralisation_close.Rdnet_by_closeness() measures a network's closeness centralization as a
single score.
mode_by_closeness() measures closeness centralization separately for each
mode of a two-mode network, returning one score per mode
(following Borgatti and Everett, 1997).
net_by_reach() measures a network's reach centralization.
net_by_decay() measures a network's decay centralization.
net_by_harmonic() measures a network's harmonic centralization.
net_by_integration() measures a network's integration 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 two-mode networks.
For two-mode networks the two modes have different theoretical maxima, so
net_by_closeness() reports a single network-level score by applying
Freeman's general centralization index over the normalized node closeness
scores, whereas mode_by_closeness() reports the per-mode scores directly.
net_by_closeness(.data, normalized = TRUE, direction = c("all", "out", "in"))
mode_by_closeness(.data, normalized = TRUE, direction = c("all", "out", "in"))
net_by_reach(.data, normalized = TRUE, cutoff = 2)
net_by_decay(.data, normalized = TRUE, decay = 0.5, direction = c("out", "in"))
net_by_integration(.data, normalized = TRUE, direction = c("in", "out"))
net_by_harmonic(.data, normalized = TRUE, cutoff = 2)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().
Logical scalar, whether scores are normalized. Different denominators may be used depending on the measure, whether the object is one-mode or two-mode, and other arguments. By default TRUE.
Character string, “out” bases the measure on outgoing ties, “in” on incoming ties, and "all" on either/the sum of the two. By default "all".
Integer scalar, the maximum path length considered.
Paths longer than this are ignored, which restricts the measure to a
node's local neighbourhood.
Where a measure is defined over all paths by default,
a negative value or NULL imposes no limit.
A proportion between 0 and 1 giving how much of a contribution
survives each additional step of distance or walk length.
Lower values discount more steeply, so that only nearby others count;
higher values discount less, so that longer walks continue to contribute.
The measures that take a decay differ in what they discount and in what
value leaves the measure in its most familiar form,
so each documents its own default.
net_by_*() functions return a network_measure scalar;
mode_by_closeness() returns a mode_measure numeric vector of length two,
giving one centralization score per mode.
Unlike reach centrality, decay and integration scores are not bounded above
by \(N-1\): integration scores scale with the network's diameter.
Freeman's index therefore cannot use the same denominator as
net_by_reach(), which would return negative values.
Instead these apply the general centralization index over the normalized
node scores, each of which lies in \([0,1]\), so the numerator's maximum
is \(N-1\) and the result is guaranteed to lie in \([0,1]\).
This is the same approach net_by_closeness() takes for two-mode networks.
Borgatti, Stephen P., and Martin G. Everett. 1997. "Network analysis of 2-mode data." Social Networks 19(3): 243-269. doi:10.1016/S0378-8733(96)00301-2
Other closeness:
measure_central_close,
measure_central_tie_close
Other centrality:
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_centralisation_between,
measure_centralisation_degree,
measure_centralisation_eigen
net_by_closeness(ison_southern_women, direction = "in")
#> # Closeness centralisation, normalized [0, 1]
#> [1] 0.193
mode_by_closeness(ison_southern_women, direction = "in")
#> # Closeness centralisation, normalized [0, 1]
#> Mode 1 Mode 2
#> 0.224 0.537
net_by_decay(ison_adolescents)
#> # Decay centralisation, normalized [0, 1]
#> [1] 0.189
net_by_integration(ison_adolescents)
#> # Integration centralisation, normalized [0, 1]
#> [1] 0.143