Plot Centrality Measures

Faceted bar plot of node-level centralities, one panel per measure. Mirrors the look of tna::plot.tna_centralities().

Usage

plot_centralities(
  x,
  measures = c("OutStrength", "InStrength", "ClosenessIn", "ClosenessOut", "Closeness",
    "Betweenness", "BetweennessRSP", "Diffusion", "Clustering"),
  by = c("state", "group"),
  reorder = TRUE,
  ncol = 3,
  scales = c("free_x", "fixed"),
  colors = NULL,
  labels = TRUE,
  ...
)

Arguments

x

An htna network, htna_group, or htna_centralities data frame from centralities_htna().

measures

Centralities to plot. Default: all nine.

by

"state" (default) gives each node its own colour; "group" colours by actor group (Human, AI, …) using htna_palette.

reorder

If TRUE, sort nodes by value within each measure.

ncol

Number of facet columns. Default 3.

scales

Facet scaling: "free_x" (default) or "fixed".

colors

Optional fill colours, recycled per group/node.

labels

If TRUE (default), draw the value next to each bar.

...

Forwarded to centralities_htna() when computing on the fly.

Value

A ggplot object.

Details

Accepts an htna network, an htna_group, or a data frame produced by centralities_htna(). For groups, bars are coloured by group within each panel.

See also

centralities_htna().

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)
plot_centralities(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.


grp <- build_htna(human_ai, actor_type = "actor_type", group = "phase")
#> 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 'cluster' (24 sessions), 'actor_type' (9 sessions)
#> 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' (18 sessions), 'actor_type' (15 sessions)
plot_centralities(grp)
#> 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.
#> 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.

# }