list_centralities: Catalogue of the Centrality Measures

Description

A tidy table of every measure centrality can compute, with the facts you need before you read a column of results: which end of the scale marks a prominent node, whether the measure needs a community partition, whether it reads edge weights, and whether it is held back from type = "all" because its cost grows steeply.

Usage

list_centralities(orientation = NULL, costly = NULL, needs_membership = NULL)

Value

A data.frame with one row per measure and the columns measure (the name to pass to centrality(measures = )), orientation ("higher" or "lower", which end of the scale marks a prominent node), mode_aware (whether the measure accepts mode and its column carries a mode suffix), needs_membership, uses_weights, and costly

(held back from type = "all"; add it with include = ). Rows are ordered by measure name.

Arguments

orientation: Keep only measures with this orientation: "higher" or "lower". Default NULL keeps both.
costly: Keep only costly measures (TRUE) or only the rest (FALSE). Default NULL keeps both.
needs_membership: Keep only measures that require a partition (TRUE) or only those that do not (FALSE). Default NULL keeps both.

Details

Twelve measures are oriented so that a low value marks the more central node, and sorting their column the usual way puts the periphery on top. Filter with orientation = "lower" to see them.

Examples

Run this code

# Every measure, with the facts needed to read its column
head(list_centralities())

# The measures where a low value marks the more central node
list_centralities(orientation = "lower")

# The measures held back from type = "all"
list_centralities(costly = TRUE)