Skip to contents

Normalize Edge Weights

Source: R/wrangle-weights.R
normalize_weights.Rd

Rescales the edge weights. Row normalization turns a transition count matrix into the transition probabilities used by TNA models.

Usage

normalize_weights(
  x,
  method = c("row", "column", "max", "sum", "minmax"),
  keep_format = FALSE,
  directed = NULL
)

Arguments

x

Network input.

method

How to rescale:

"row"

(default) each row sums to 1

"column"

each column sums to 1

"max"

divide by the largest absolute weight

"sum"

divide by the sum of the weight matrix, in which each undirected edge appears twice

"minmax"

rescale the non-zero weights to [0, 1]

keep_format

Logical. Return the input format when TRUE.

directed

Logical or NULL. If NULL (default), auto-detect.

Value

A cograph_network with rescaled weights, or the input format when keep_format = TRUE.

Details

A row or column whose total is zero is left at zero. Zero totals, and a zero denominator for "max" or "sum", raise a cograph_zero_norm warning.

"minmax" maps the weakest edge to .Machine$double.eps. A weight of exactly 0 would remove the edge, because 0 stores "no edge". When all weights are equal they all become 1.

"max", "sum" and "minmax" rescale each edge independently and keep any extra edge columns. "row" and "column" scale an edge by a total that differs at its two endpoints. They break symmetry, so an undirected input is returned as a directed network.

See also

binarize, invert_weights, symmetrize

Examples

normalize_weights(regulation_net, method = "row")
#> Cograph network: 10 nodes, 30 edges ( directed )
#> Source: matrix 
#>   Nodes (10): Explore, Plan, Monitor, Adapt, Reflect, Discuss, ... +4 more
#>   Edges: 30 / 90 (density: 33.3%)
#>   Weights: [0.082, 0.750]  |  mean: 0.333
#>   Strongest edges:
#>     Reflect -> Monitor  0.750
#>     Synthesize -> Reflect  0.700
#>     Monitor -> Create  0.698
#>     Explore -> Reflect  0.565
#>     Evaluate -> Adapt  0.518
#> Layout: none 
#>   Use as.data.frame() for the edge table, as.data.frame(what = "nodes") for the nodes.