Computes a fixed panel of centrality measures via cograph::cograph and
returns them as a tidy data frame: one row per node, one column per
measure (plus node, an actor column when the htna partition is
set, and a group column when the input is an htna_group).
Usage
centralities_htna(
x,
measures = c("OutStrength", "InStrength", "ClosenessIn", "ClosenessOut", "Closeness",
"Betweenness", "BetweennessRSP", "Diffusion", "Clustering"),
...
)Arguments
- x
An htna network from
build_htna()or anhtna_group.- measures
Character vector of measure names. Defaults to all nine.
- ...
Forwarded to the underlying cograph centrality functions.
Details
The measures are: OutStrength, InStrength, ClosenessIn,
ClosenessOut, Closeness, Betweenness, BetweennessRSP,
Diffusion, Clustering. Path-based measures (closeness and
betweenness variants) are computed with invert_weights = TRUE,
since in transition networks larger weight = stronger link =
shorter distance. Strength and the closed-form measures
(Diffusion, Clustering) use raw weights.
Mapping from measure name to cograph implementation:
OutStrength→cograph::centrality_outstrength()InStrength→cograph::centrality_instrength()ClosenessIn→cograph::centrality_incloseness()(inverted)ClosenessOut→cograph::centrality_outcloseness()(inverted)Closeness→cograph::centrality_closeness()(inverted)Betweenness→cograph::centrality_betweenness()(inverted)BetweennessRSP→cograph::centrality_current_flow_betweenness()Diffusion→cograph::centrality_diffusion()Clustering→cograph::centrality_transitivity()
Examples
# \donttest{
data(human_ai)
net <- build_htna(human_ai, actor_type = "actor_type")
#> Warning: A network with one long sequence is not recommended and can't be validated using bootstrap and other confirmatory testings.
#> Metadata aggregated per session: ties resolved by first occurrence in 'session_date' (1 sessions), 'cluster' (42 sessions), 'actor_type' (24 sessions)
centralities_htna(net)
#> Note: Weights inverted (1/w^1) for path-based measures (invert_weights=TRUE). Higher weights = shorter paths.
#> Note: Weights inverted (1/w^1) for path-based measures (invert_weights=TRUE). Higher weights = shorter paths.
#> Note: Weights inverted (1/w^1) for path-based measures (invert_weights=TRUE). Higher weights = shorter paths.
#> Note: Weights inverted (1/w^1) for path-based measures (invert_weights=TRUE). Higher weights = shorter paths.
#> node actor OutStrength InStrength ClosenessIn ClosenessOut Closeness
#> 1 Ask AI 0.9817642 1.5266008 0.012587425 0.006192077 0.01670617
#> 2 Check Human 0.9499230 0.7522288 0.008035193 0.006492281 0.01340661
#> 3 Delegate AI 1.0000000 0.1740318 0.003015314 0.007567519 0.01315340
#> 4 Execute AI 0.9256198 2.0338718 0.016720393 0.006367002 0.01784416
#> 5 Frustrate Human 0.8861885 0.9672100 0.010511066 0.006322199 0.01169411
#> 6 Inquire Human 0.9671362 0.5147284 0.006592669 0.007311931 0.01399616
#> 7 Plan AI 0.9967742 1.2496499 0.012325008 0.006926899 0.01675952
#> 8 Refine Human 1.0000000 0.4703420 0.005943026 0.006143417 0.01250189
#> 9 Repair AI 0.9960474 0.1995057 0.002655997 0.007958047 0.01404200
#> 10 Report AI 0.9225146 0.5246300 0.005125410 0.007099789 0.01180054
#> 11 Request Human 0.9329032 1.6926517 0.016089323 0.005980693 0.01812498
#> 12 Specify Human 0.8991424 1.3525625 0.013064153 0.006197358 0.01619607
#> Betweenness BetweennessRSP Diffusion Clustering
#> 1 13 91 8.778380 0.1543463
#> 2 0 42 8.354335 0.2074548
#> 3 0 3 9.003808 0.1868121
#> 4 29 118 8.186741 0.1359230
#> 5 2 61 7.948727 0.2014196
#> 6 14 27 8.615216 0.1703483
#> 7 25 63 8.715568 0.1531488
#> 8 0 23 8.751161 0.2393259
#> 9 0 1 8.852837 0.2039989
#> 10 0 17 8.186046 0.1557107
#> 11 19 110 8.181829 0.1842378
#> 12 15 88 8.008017 0.2112340
# }