nd.edd {NetworkDistance}

Edge Difference Distance

Description

It is of the most simplest form that Edge Difference Distance (EDD) takestwo adjacency matrices and takes Frobenius norm of their differnces.

Usage

nd.edd(A, out.dist = TRUE)

Arguments

A

a list of length N containing (M\times M) adjacency matrices.

out.dist

a logical; TRUE for computed distance matrix as a dist object.

Value

a named list containing

D

an (N\times N) matrix or dist object containing pairwise distance measures.

References

Hammond DK, Gur Y, Johnson CR (2013). “Graph Diffusion Distance: A Difference Measure for Weighted Graphs Based on the Graph Laplacian Exponential Kernel.” In Proceedings of the IEEE global conference on information and signal processing (GlobalSIP'13), 419–422.

Examples

## load example data
data(graph20)

## compute distance matrix
output = nd.edd(graph20, out.dist=FALSE)

## visualize
opar <- par(no.readonly=TRUE)
par(pty="s")
image(output$D[,20:1], main="two group case", axes=FALSE, col=gray(0:32/32))
par(opar)