Computes centrality measures for edges in a network and returns a tidy data frame. Unlike node centrality, these measures describe edge importance.
Usage
edge_centrality(
x,
measures = "all",
weighted = TRUE,
directed = NULL,
cutoff = -1,
invert_weights = NULL,
alpha = 1,
digits = NULL,
sort_by = NULL,
...
)
edge_betweenness(x, ...)Arguments
- x
Network input (matrix, igraph, network, cograph_network, tna object)
- measures
Which measures to calculate. Default "all" calculates all available edge measures. Options: "betweenness", "weight", "overlap", "simmelian", "reciprocity".
- weighted
Logical. Use edge weights if available. Default TRUE.
- directed
Logical or NULL. If NULL (default), auto-detect from matrix symmetry. Set TRUE to force directed, FALSE to force undirected.
- cutoff
Maximum path length for betweenness. Default -1 (no limit).
- invert_weights
Logical or NULL. Invert weights for path-based measures? Default NULL (auto-detect: TRUE for tna objects, FALSE otherwise).
- alpha
Numeric. Exponent for weight inversion. Default 1.
- digits
Integer or NULL. Round numeric columns. Default NULL.
- sort_by
Character or NULL. Column to sort by (descending). Default NULL.
- ...
Additional arguments passed to
to_igraph
Value
A data frame with columns from, to, and one column
per requested measure.
Named numeric vector of edge betweenness values (named by
"from->to").
Details
Edge measures available:
- betweenness
Number of shortest paths passing through the edge.
- weight
Original edge weight.
- overlap
Jaccard neighborhood overlap of edge endpoints.
- simmelian
Number of triangles the edge participates in.
- reciprocity
Whether the reverse edge exists (directed only). Adds columns:
reciprocated,reverse_weight,weight_ratio.
Examples
# Create test network
mat <- matrix(c(0,1,1,0, 1,0,1,1, 1,1,0,0, 0,1,0,0), 4, 4)
rownames(mat) <- colnames(mat) <- c("A", "B", "C", "D")
# All edge measures
edge_centrality(mat)
#> from to weight betweenness overlap shared_neighbors triangles
#> 1 A B 1 2 0.5 1 1
#> 2 A C 1 1 1.0 1 1
#> 3 B C 1 2 0.5 1 1
#> 4 B D 1 3 0.0 0 0
# Just betweenness
edge_centrality(mat, measures = "betweenness")
#> from to betweenness
#> 1 A B 2
#> 2 A C 1
#> 3 B C 2
#> 4 B D 3
# Sort by betweenness to find bridge edges
edge_centrality(mat, sort_by = "betweenness")
#> from to weight betweenness overlap shared_neighbors triangles
#> 1 B D 1 3 0.0 0 0
#> 2 A B 1 2 0.5 1 1
#> 3 B C 1 2 0.5 1 1
#> 4 A C 1 1 1.0 1 1
mat <- matrix(c(0,1,1,0, 1,0,1,1, 1,1,0,0, 0,1,0,0), 4, 4)
rownames(mat) <- colnames(mat) <- c("A", "B", "C", "D")
edge_betweenness(mat)
#> A->B A->C B->C B->D
#> 2 1 2 3