Layer Similarity

Computes similarity between two network layers.

Usage

layer_similarity(
  A1,
  A2,
  method = c("jaccard", "overlap", "hamming", "cosine", "pearson")
)

lsim(A1, A2, method = c("jaccard", "overlap", "hamming", "cosine", "pearson"))

Arguments

A1

First adjacency matrix

A2

Second adjacency matrix

method

Comparison method: "jaccard" (default), "overlap", "hamming", "cosine" or "pearson"

Value

A single numeric value. All methods except "hamming" return a similarity (higher = more alike); "hamming" returns a distance - the number of matrix cells whose edge presence differs between the two layers - so lower means more alike and the value is not bounded by 1. NA is returned when the denominator is undefined ("jaccard" with no edges in either layer, "overlap" with an empty layer, "cosine" with an all-zero layer).

Details

"jaccard", "overlap" and "hamming" compare edge presence (A > 0) and therefore ignore weights; "cosine" and "pearson" are computed on the raw cell values. The two matrices must have identical dimensions.

Examples

A1 <- matrix(c(0,1,1,0, 1,0,0,1, 1,0,0,1, 0,1,1,0), 4, 4)
A2 <- matrix(c(0,1,0,0, 1,0,1,0, 0,1,0,1, 0,0,1,0), 4, 4)

layer_similarity(A1, A2, "jaccard")  # Edge overlap
#> [1] 0.4
layer_similarity(A1, A2, "cosine")   # Weight similarity
#> [1] 0.5773503