close_centrality: Measures of closeness-like centrality and centralisation

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Measures of closeness-like centrality and centralisation

Description

Measures of closeness-like centrality and centralisation

Usage

node_closeness(.data, normalized = TRUE, direction = "out", cutoff = NULL)

node_reach(.data, normalized = TRUE, k = 2)

node_harmonic(.data, normalized = TRUE, k = -1)

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)

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
normalized	Logical scalar, whether the centrality scores are normalized. Different denominators are used depending on whether the object is one-mode or two-mode, 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 two-mode 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

Functions

• node_closeness(): Calculate the closeness centrality of nodes in a network

• node_reach(): Calculate nodes' reach centrality or how many nodes they can reach within k steps

• node_harmonic(): Calculate nodes' harmonic centrality or valued centrality. This is thought to behave better than reach centrality for disconnected networks.

• tie_closeness(): Calculate the closeness of each edge to each other edge in the network.

• network_closeness(): Calculate a network's closeness centralization

• network_reach(): Calculate a network's reach centralization

• network_harmonic(): Calculate a network's harmonic centralization

References

Marchiori, M, and V Latora. 2000. "Harmony in the small-world". Physica A 285: 539-546.

Dekker, Anthony. 2005. "Conceptual distance in social network analysis". Journal of Social Structure 6(3).

See Also

Other measures: between_centrality, closure, cohesion(), degree_centrality, eigenv_centrality, features, heterogeneity, hierarchy, holes

Other centrality: between_centrality, degree_centrality, eigenv_centrality

Examples

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")

migraph documentation built on Nov. 2, 2023, 5:47 p.m.