layer_similarity: Layer Similarity

In cograph: Analysis and Visualization of Complex Networks

Layer Similarity

Description

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"

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.

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).

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
layer_similarity(A1, A2, "cosine")   # Weight similarity

cograph documentation built on Sept. 30, 2026, 5:08 p.m.