Compute Markov diffusion kernel via eigen decomposition

Efficient computation of the Markov diffusion kernel for a graph represented by a sparse adjacency matrix. For large graphs, uses RSpectra to compute only the leading k eigenpairs of the normalized transition matrix.

Usage

compute_diffusion_kernel(A, t, k = NULL, symmetric = TRUE)

Arguments

A

Square sparse adjacency matrix (dgCMatrix) of an undirected, weighted graph with non-negative entries.

t

Diffusion time parameter (positive scalar).

k

Number of leading eigenpairs to compute. If NULL, performs full eigendecomposition.

symmetric

If TRUE (default), uses symmetric normalization to guarantee real eigenvalues.

Value

dgCMatrix representing the diffusion kernel matrix.

Examples

library(Matrix)
A <- sparseMatrix(i = c(1, 2, 3, 4), j = c(2, 3, 4, 5), 
                  x = c(1, 1, 1, 1), dims = c(5, 5))
A <- A + t(A)  # Make symmetric

K <- compute_diffusion_kernel(A, t = 0.5)

K_approx <- compute_diffusion_kernel(A, t = 0.5, k = 3)