Compute Centrality Measures for a Network — net

Computes centrality measures from a netobject, netobject_group, mcml, or cograph_network. The built-in measures match tna::centralities() without importing tna or igraph: strength is taken from the weight matrix directly, and the path-based measures (betweenness, closeness) come from all-pairs shortest paths computed in-package by Floyd-Warshall. The only intentional default difference from tna is that Diffusion is range-normalized by default.

Usage

net_centrality(
  x,
  measures = NULL,
  loops = FALSE,
  normalize = FALSE,
  invert = TRUE,
  normalize_diffusion = TRUE,
  centrality_fn = NULL,
  ...
)

# S3 method for class 'net_centrality'
plot(
  x,
  reorder = TRUE,
  ncol = 3L,
  type = c("bar", "line", "heatmap"),
  scales = c("free_x", "fixed"),
  profile_scale = c("measure", "none"),
  labels = TRUE,
  drop_zero = FALSE,
  ...
)

# S3 method for class 'net_centrality_group'
plot(
  x,
  reorder = TRUE,
  ncol = 3L,
  type = c("bar", "line", "delta"),
  scales = c("free_x", "fixed"),
  palette = "Set2",
  profile_scale = c("measure", "none"),
  labels = FALSE,
  drop_zero = FALSE,
  ...
)

Arguments

x: A netobject, netobject_group, mcml, or cograph_network. For the plot() method: an object of class net_centrality or net_centrality_group.
measures: Character vector. Centrality measures to compute. Defaults to c("InStrength", "Betweenness", "Diffusion"). Pass "all" for every built-in measure: "OutStrength", "InStrength", "ClosenessIn", "ClosenessOut", "Closeness", "Betweenness", "BetweennessRSP", "Diffusion", and "Clustering". The legacy aliases "InCloseness" and "OutCloseness" are also accepted.
loops: Logical. Include self-loops (diagonal) in computation? Default: FALSE.
normalize: Logical. Range-normalize all requested measures using the same transformation as tna::centralities(normalize = TRUE). Default: FALSE.
invert: Logical. Invert weights for shortest-path measures? Default: TRUE, matching tna.
normalize_diffusion: Logical. Range-normalize Diffusion even when normalize = FALSE. Default: TRUE.
centrality_fn: Optional function. Custom centrality function that takes a weight matrix and returns a named list of centrality vectors.
...: Additional arguments (ignored). In plot.net_centrality() and plot.net_centrality_group(): Additional arguments ignored.
reorder: In plot.net_centrality(): Logical. Reorder states within each centrality panel by centrality value. Default: TRUE. In plot.net_centrality_group(): Logical. Reorder states by their mean value within each centrality panel. Default: TRUE.
ncol: Integer. Number of facet columns. Default: 3.
type: In plot.net_centrality(): Plot type. "bar" shows one faceted horizontal bar chart per measure; "line" shows state profiles as lines across measures (the value "profile" is still accepted as an alias); "heatmap" shows a states-by-measures tile grid, each measure scaled to 0–1 for cross-measure comparability with the raw value printed in the tile. Default: "bar". In plot.net_centrality_group(): Plot type. "bar" shows grouped bars within each measure; "line" facets by state and draws one line per group across centrality measures ("profile" is accepted as an alias); "delta" draws a diverging bar of group differences. With two groups it is the per-state difference (second group minus first); with three or more groups it is each group's deviation from the per-state group mean, so the largest gaps stand out either way. Default: "bar".
scales: Facet scale mode. "free_x" (default) uses free centrality axes; "fixed" keeps a common centrality axis.
profile_scale: Scaling used by type = "line". "measure" (default) rescales each centrality measure to 0–1 before drawing cross-measure profiles; "none" uses raw values.
labels: In plot.net_centrality(): Logical. Add compact value labels. Default: TRUE. In plot.net_centrality_group(): Logical. Add compact value labels. Default: FALSE.
drop_zero: In plot.net_centrality(): Logical. Drop measures whose values are all (near) zero so empty panels do not waste space. Default: FALSE (every requested measure is shown). In plot.net_centrality_group(): Logical. Drop measures whose values are all (near) zero so empty panels do not waste space. Default: FALSE.
palette: Brewer palette for groups. Default: "Set2".

Value

For a netobject or cograph_network: a net_centrality data frame, one row per node, with a state column and one further column per requested measure (node names are also the row names). For a netobject_group or an mcml: a net_centrality_group list of such data frames, one per group.

In plot.net_centrality() and plot.net_centrality_group(): A ggplot object.

References

Freeman, L. C. (1978). Centrality in social networks: conceptual clarification. Social Networks, 1(3), 215–239. (betweenness, closeness)

Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: generalizing degree and shortest paths. Social Networks, 32(3), 245–251. (weighted strength and geodesics)

Kivimaki, I., Lebichot, B., Saramaki, J. & Saerens, M. (2016). Two betweenness centrality measures based on randomized shortest paths. Scientific Reports, 6, 19668. (BetweennessRSP)

Banerjee, A., Chandrasekhar, A. G., Duflo, E. & Jackson, M. O. (2013). The diffusion of microfinance. Science, 341(6144), 1236498. (Diffusion)

Onnela, J.-P., Saramaki, J., Kertesz, J. & Kaski, K. (2005). Intensity and coherence of motifs in weighted complex networks. Physical Review E, 71, 065103. (Clustering)

Examples

seqs <- data.frame(
  V1 = c("A","B","A","C"), V2 = c("B","C","B","A"),
  V3 = c("C","A","C","B"))
net <- build_network(seqs, method = "relative")
net_centrality(net)
#> centralities computed excluding loops (diagonal). Pass `loops = TRUE` to include self-transitions.
#>   state InStrength Betweenness Diffusion
#> A     A          1           1       NaN
#> B     B          1           1       NaN
#> C     C          1           1       NaN