Centrality Stability Coefficient (CS-coefficient)

Estimates the stability of centrality indices under case-dropping. For each drop proportion, sequences are randomly removed and the network is re-estimated. The correlation between the original and subset centrality values is computed. The CS-coefficient is the maximum proportion of cases that can be dropped while maintaining a correlation above threshold in at least certainty of bootstrap samples.

For transition methods, uses pre-computed per-sequence count matrices for fast resampling. Strength centralities (InStrength, OutStrength) are computed directly from the matrix without igraph.

Usage

centrality_stability(
  x,
  measures = c("InStrength", "OutStrength", "Betweenness"),
  iter = 1000L,
  drop_prop = seq(0.1, 0.9, by = 0.1),
  threshold = 0.7,
  certainty = 0.95,
  method = "pearson",
  centrality_fn = NULL,
  loops = FALSE,
  seed = NULL
)

Arguments

x

A netobject from build_network.

measures

Character vector. Centrality measures to assess. Built-in: "InStrength", "OutStrength", "Betweenness", "InCloseness", "OutCloseness", "Closeness". Custom measures beyond these require centrality_fn. Default: c("InStrength", "OutStrength", "Betweenness").

iter

Integer. Number of bootstrap iterations per drop proportion (default: 1000).

drop_prop

Numeric vector. Proportions of cases to drop (default: seq(0.1, 0.9, by = 0.1)).

threshold

Numeric. Minimum correlation to consider stable (default: 0.7).

certainty

Numeric. Required proportion of iterations above threshold (default: 0.95).

method

Character. Correlation method: "pearson", "spearman", or "kendall" (default: "pearson").

centrality_fn

Optional function. A custom centrality function that takes a weight matrix and returns a named list of centrality vectors. When NULL (default), only "InStrength" and "OutStrength" are computed via colSums/rowSums. When provided, the function is called as centrality_fn(mat) and should return a named list (e.g., list(Betweenness = ..., Closeness = ...)).

loops

Logical. If FALSE (default), self-loops (diagonal) are excluded from centrality computation. This does not modify the stored matrix.

seed

Integer or NULL. RNG seed for reproducibility.

Value

An object of class "net_stability" containing:

cs

Named numeric vector of CS-coefficients per measure.

correlations

Named list of matrices (iter x n_prop) of correlation values per measure.

measures

Character vector of measures assessed.

drop_prop

Drop proportions used.

threshold

Stability threshold.

certainty

Required certainty level.

iter

Number of iterations.

method

Correlation method.

See also

build_network, reliability

Examples

# \donttest{
seqs <- data.frame(
  V1 = sample(LETTERS[1:4], 30, TRUE), V2 = sample(LETTERS[1:4], 30, TRUE),
  V3 = sample(LETTERS[1:4], 30, TRUE), V4 = sample(LETTERS[1:4], 30, TRUE)
)
net <- build_network(seqs, method = "relative")
cs <- centrality_stability(net, iter = 100, seed = 42,
  measures = c("InStrength", "OutStrength"))
print(cs)
#> Centrality Stability (100 iterations, threshold = 0.7)
#>   Drop proportions: 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9
#> 
#>   CS-coefficients:
#>     InStrength       0.00
#>     OutStrength      0.20
# }