net_prune: Prune a Network's Edges

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Prune a Network's Edges

Description

Removes weak or non-significant edges from a network, keeping a record so the operation can be reversed. This is Nestimate's counterpart of tna::prune(); the net_ prefix avoids a name clash with tna::prune().

Usage

net_prune(
  x,
  method = "threshold",
  threshold = 0.1,
  lowest = 0.05,
  level = 0.5,
  boot = NULL,
  ...
)

## S3 method for class 'netobject'
net_prune(
  x,
  method = "threshold",
  threshold = 0.1,
  lowest = 0.05,
  level = 0.5,
  boot = NULL,
  ...
)

## S3 method for class 'netobject_group'
net_prune(
  x,
  method = "threshold",
  threshold = 0.1,
  lowest = 0.05,
  level = 0.5,
  boot = NULL,
  ...
)

## Default S3 method:
net_prune(
  x,
  method = "threshold",
  threshold = 0.1,
  lowest = 0.05,
  level = 0.5,
  boot = NULL,
  ...
)

Arguments

x	A netobject or netobject_group.
method	One of "threshold", "lowest", "disparity", "bootstrap". Default "threshold".
threshold	Numeric cut-off for method = "threshold". Default 0.1.
lowest	Quantile (0-1) for method = "lowest". Default 0.05.
level	Significance level (0-1) for method = "disparity". Default 0.5.
boot	Optional precomputed net_bootstrap for method = "bootstrap".
...	Passed to bootstrap_network when method = "bootstrap".

Details

Pruning is non-destructive: the pruned network carries a "pruning" attribute holding the original weights, the pruned weights, the parameters used, and a tidy table of removed edges. Use net_deprune to restore the original weights and net_reprune to re-apply the pruning, both without recomputation. net_pruning_details reports what was removed.

Methods:

"threshold"

Remove edges with weight \le threshold.

"lowest"

Remove the lowest lowest quantile of non-zero edges.

"disparity"

Serrano disparity-filter backbone at significance level.

"bootstrap"

Remove edges deemed non-significant by bootstrap_network (pass a precomputed result via boot, or extra bootstrap arguments via ...).

For "threshold", "lowest", and "disparity" an edge is dropped only when its removal leaves the network weakly connected.

Diagonal self-loops (self-transitions) are observed data: they are counted equally when computing the cut-off but are never removed by any method. (This is a deliberate divergence from tna::prune(), which prunes self-loops like any other edge.)

Value

The input network (or group) with pruned $weights and a "pruning" attribute. Class is unchanged.

See Also

net_deprune, net_reprune, net_pruning_details

Examples

seqs <- data.frame(
  V1 = c("A","B","A","C","B"), V2 = c("B","C","B","A","C"),
  V3 = c("C","A","C","B","A"))
net    <- build_network(seqs, method = "relative")
pruned <- net_prune(net, method = "threshold", threshold = 0.2)
net_pruning_details(pruned)

Nestimate documentation built on July 11, 2026, 1:09 a.m.