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Symmetrize a Directed Network

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

Combines each pair of opposite arcs into one undirected edge. The result is an undirected network. Self-loops are kept unchanged.

Usage

symmetrize(
  x,
  method = c("max", "min", "mean", "sum", "mutual", "upper", "lower"),
  keep_format = FALSE,
  directed = NULL
)

Arguments

x

Network input.

method

How to combine w[i, j] and w[j, i]:

"max"

(default) the larger of the two; on a binary network this is sna's "weak" rule

"min"

the smaller of the two

"mean"

their average

"sum"

their total

"mutual"

keep only reciprocated pairs, taking the smaller weight; on a binary network this is sna's "strong" rule

"upper"

take the upper triangle and mirror it

"lower"

take the lower triangle and mirror it

keep_format

Logical. Return the input format when TRUE.

directed

Logical or NULL. Directedness to read the input with; the result is always undirected.

Value

An undirected cograph_network, or the input format when keep_format = TRUE. The weight matrix satisfies isSymmetric(). Zero is how this representation stores "no edge", so any pair whose combined weight is exactly zero disappears: every unreciprocated arc under method = "mutual", and a cancelling pair under "sum". A cograph_edges_dropped warning says how many.

Details

"max", "min", "mean" and "sum" combine two values only where both arcs exist. An unreciprocated edge keeps its own weight. In a signed network an unreciprocated negative edge is therefore kept. "mutual" keeps only reciprocated edges.

References

Butts, C. T. (2008). Social network analysis with sna. Journal of Statistical Software, 24(6), 1–51.

See also

to_undirected, normalize_weights

Examples

symmetrize(regulation_net, method = "mean")
#> Cograph network: 10 nodes, 27 edges ( undirected )
#> Source: matrix 
#>   Nodes (10): Explore, Plan, Monitor, Adapt, Reflect, Discuss, ... +4 more
#>   Edges: 27 / 45 (density: 60.0%)
#>   Weights: [0.070, 0.490]  |  mean: 0.267
#>   Strongest edges:
#>     Plan -- Evaluate  0.490
#>     Monitor -- Share  0.490
#>     Adapt -- Evaluate  0.430
#>     Reflect -- Synthesize  0.420
#>     Plan -- Discuss  0.400
#> Layout: none 
#>   Use as.data.frame() for the edge table, as.data.frame(what = "nodes") for the nodes.