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Centrality Stability for an htna Network

Source: R/centrality_stability_htna.R
centrality_stability_htna.Rd

Thin wrapper around Nestimate::centrality_stability() that validates the input is an htna network (or htna_group), forwards the call, and tags the result with an htna_stability class so callers can dispatch on the htna form.

Usage

centrality_stability_htna(
  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

An htna network from build_htna() or an htna_group.

measures

Character vector of centrality measures to assess. Default c("InStrength", "OutStrength", "Betweenness") — the three measures whose values are bit-equal between htna's cograph engine and Nestimate's default centrality implementation. To assess htna's nine measures (including the three that differ between engines: BetweennessRSP, Diffusion, Clustering), pass them explicitly together with a custom centrality_fn.

iter

Integer. Number of resamples per drop proportion. Default 1000.

drop_prop

Numeric vector. Proportions of sessions to drop. Default seq(0.1, 0.9, by = 0.1).

threshold

Numeric in [0, 1]. Correlation threshold for the case-dropping stability coefficient. Default 0.7.

certainty

Numeric in [0, 1]. Probability of meeting the threshold required for a drop proportion to count as stable. Default 0.95.

method

Correlation method, one of "pearson", "spearman", "kendall". Default "pearson".

centrality_fn

Optional function to compute centralities. Default NULL (use Nestimate's internal implementation). See Nestimate::centrality_stability() for the expected signature.

loops

Logical. Whether to retain self-loops. Default FALSE.

seed

Optional integer seed for reproducibility. Default NULL (no seed reset).

Value

For a single htna network, an object of class c("htna_stability", "net_stability") with the same components as Nestimate::centrality_stability(): cs (the stability coefficients, one per measure), correlations (per-drop-proportion correlation traces), and the parameters that were used. For an htna_group, a named list of such objects with class c("htna_stability_group", "list").

Details

For an htna_group, the call is iterated per cohort and the result is a named list (one htna_stability per cohort), with class c("htna_stability_group", "list").

See also

Nestimate::centrality_stability(), bootstrap_htna(), reliability_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)
cs  <- centrality_stability_htna(net, iter = 30, seed = 1)
cs$cs
#>  InStrength OutStrength Betweenness 
#>         0.9         0.9         0.8 
# }