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Invert Edge Weights (Similarity to Distance and Back)

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

Turns strong ties into short distances. Path-based measures treat weights as costs, so similarity weights are inverted before such measures are computed.

Usage

invert_weights(
  x,
  method = c("reciprocal", "max_minus", "reflect"),
  keep_format = FALSE,
  directed = NULL
)

Arguments

x

Network input.

method

How to invert:

"reciprocal"

(default) 1 / w, the standard similarity-to-distance map.

"max_minus"

max(w) - w. The strongest edge becomes zero and is therefore dropped; a cograph_edges_dropped warning says how many.

"reflect"

max(w) + min(w) - w. Reverses the order of the weights and keeps every edge when all weights are positive.

keep_format

Logical. Return the input format when TRUE.

directed

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

Value

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

See also

normalize_weights, shortest_paths

Examples

invert_weights(regulation_net, method = "reciprocal")
#> 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: [2.041, 20.000]  |  mean: 5.421
#>   Strongest edges:
#>     Reflect -> Explore  20.000
#>     Synthesize -> Monitor  14.286
#>     Evaluate -> Reflect  14.286
#>     Synthesize -> Plan  9.091
#>     Plan -> Monitor  7.692
#> Layout: none 
#>   Use as.data.frame() for the edge table, as.data.frame(what = "nodes") for the nodes.