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.
Arguments
- orientation
Keep only measures with this orientation:
"higher"or"lower". DefaultNULLkeeps both.- costly
Keep only costly measures (
TRUE) or only the rest (FALSE). DefaultNULLkeeps both.- needs_membership
Keep only measures that require a partition (
TRUE) or only those that do not (FALSE). DefaultNULLkeeps both.
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.
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.
See also
centrality to compute them,
centrality_degree and the other one-measure verbs.
Examples
# Every measure, with the facts needed to read its column
head(list_centralities())
#> measure orientation mode_aware needs_membership uses_weights
#> 1 access_information lower FALSE FALSE FALSE
#> 2 adaptive_leaderrank higher FALSE FALSE FALSE
#> 3 alpha higher TRUE FALSE TRUE
#> 4 authority higher FALSE FALSE TRUE
#> 5 average_distance lower TRUE FALSE TRUE
#> 6 barycenter higher TRUE FALSE TRUE
#> costly
#> 1 FALSE
#> 2 FALSE
#> 3 FALSE
#> 4 FALSE
#> 5 FALSE
#> 6 FALSE
# The measures where a low value marks the more central node
list_centralities(orientation = "lower")
#> measure orientation mode_aware needs_membership uses_weights
#> 1 access_information lower FALSE FALSE FALSE
#> 2 average_distance lower TRUE FALSE TRUE
#> 3 constraint lower FALSE FALSE TRUE
#> 4 eccentricity lower TRUE FALSE TRUE
#> 5 heatmap lower TRUE FALSE FALSE
#> 6 hide_information lower FALSE FALSE FALSE
#> 7 local_dimension lower TRUE FALSE FALSE
#> 8 local_dimension_fixed lower TRUE FALSE FALSE
#> 9 local_entropy lower TRUE FALSE FALSE
#> 10 local_volume_dimension lower TRUE FALSE FALSE
#> 11 second_order lower FALSE FALSE FALSE
#> 12 wiener lower TRUE FALSE TRUE
#> costly
#> 1 FALSE
#> 2 FALSE
#> 3 FALSE
#> 4 FALSE
#> 5 FALSE
#> 6 FALSE
#> 7 FALSE
#> 8 FALSE
#> 9 FALSE
#> 10 FALSE
#> 11 FALSE
#> 12 FALSE
# The measures held back from type = "all"
list_centralities(costly = TRUE)
#> measure orientation mode_aware needs_membership
#> 1 bridging_capital higher FALSE FALSE
#> 2 controlrank higher FALSE FALSE
#> 3 dynamical_importance higher FALSE FALSE
#> 4 entropy_variation_betweenness higher FALSE FALSE
#> 5 epc higher FALSE FALSE
#> 6 exogenous higher TRUE FALSE
#> 7 extended_local_bridging higher FALSE FALSE
#> 8 fragmentation higher TRUE FALSE
#> 9 iec higher FALSE FALSE
#> 10 infection higher FALSE FALSE
#> 11 linerank higher FALSE FALSE
#> 12 mcc higher FALSE FALSE
#> 13 node_contraction_improved higher FALSE FALSE
#> 14 random_walk_decay higher FALSE FALSE
#> 15 resistance_curvature higher FALSE FALSE
#> 16 rsp_betweenness higher FALSE FALSE
#> 17 trust_pagerank higher FALSE FALSE
#> 18 two_way_rw higher FALSE FALSE
#> uses_weights costly
#> 1 TRUE TRUE
#> 2 TRUE TRUE
#> 3 TRUE TRUE
#> 4 FALSE TRUE
#> 5 FALSE TRUE
#> 6 FALSE TRUE
#> 7 FALSE TRUE
#> 8 TRUE TRUE
#> 9 FALSE TRUE
#> 10 FALSE TRUE
#> 11 TRUE TRUE
#> 12 FALSE TRUE
#> 13 FALSE TRUE
#> 14 TRUE TRUE
#> 15 TRUE TRUE
#> 16 TRUE TRUE
#> 17 FALSE TRUE
#> 18 TRUE TRUE