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Multiple Graph CONE-Align: Consensus Optimization for Multi-Domain Node Embedding Alignment

Source: R/all_generic.R, R/cone_align_multiple.R
cone_align_multiple.Rd

Performs CONE-Align on three or more graph domains simultaneously. Extends the pairwise CONE-Align algorithm to handle multiple graphs through iterative alignment to a common reference frame.

Usage

cone_align_multiple(data, ...)

# S3 method for class 'hyperdesign'
cone_align_multiple(
  data,
  ref_idx = 1L,
  preproc = center(),
  ncomp = 10,
  sigma = 0.73,
  lambda = 0.1,
  use_laplacian = TRUE,
  solver = c("linear", "auction"),
  max_iter = 30,
  tol = 0.01,
  knn = NULL,
  ...
)

# S3 method for class 'list'
cone_align_multiple(data, ...)

# Default S3 method
cone_align_multiple(data, ...)

Arguments

data

A list containing three or more matrices (nodes x features)

...

Additional arguments passed to hyperdesign method

ref_idx

Which domain acts as initial reference (default: 1)

preproc

Preprocessing function to apply to the data (default: center())

ncomp

Number of embedding dimensions (default: 10)

sigma

Diffusion parameter for graph construction (default: 0.73)

lambda

Regularization parameter for numerical stability (default: 0.1)

use_laplacian

Whether to use Laplacian normalization (default: TRUE)

solver

Assignment algorithm: "linear" for exact assignment (default), "auction" for large-scale approximation

max_iter

Maximum number of iterations (default: 30)

tol

Convergence tolerance for assignment changes (default: 0.01)

knn

Number of nearest neighbors for graph construction (default: adaptive)

Value

A multiblock_biprojector object containing alignment results for all graphs

See also

cone_align for pairwise alignment, cone_align_multiple.hyperdesign for detailed documentation

Examples

# \donttest{
set.seed(1)
X1 <- matrix(rnorm(60), 30, 2)
X2 <- matrix(rnorm(60), 30, 2)
X3 <- matrix(rnorm(60), 30, 2)
result <- cone_align_multiple(list(X1, X2, X3), ncomp = 2)
#> Converged after 3 iterations.
#> Converged after 2 iterations.
# }