Build a K-Nearest Neighbor Graph — fastMakeKNNGraph • jvecfor

Convenience wrapper that calls fastFindKNN then delegates graph construction to bluster::neighborsToKNNGraph.

Usage

fastMakeKNNGraph(
  X,
  k = 15L,
  type = c("ann", "knn"),
  metric = c("euclidean", "cosine", "dot_product"),
  num.threads = NULL,
  BPPARAM = BiocParallel::bpparam(),
  ef.search = 0L,
  M = 16L,
  oversample.factor = 1,
  pq.subspaces = 0L,
  verbose = getOption("jvecfor.verbose", FALSE),
  directed = FALSE,
  ...
)

Arguments

X: A numeric matrix, data.frame, or sparse matrix (Matrix::dgCMatrix) with rows = cells, cols = features/PCs.
k: Integer. Number of nearest neighbors. Default 15.
type: Character. "ann" or "knn". Default "ann".
metric: Character. "euclidean", "cosine", or "dot_product". Default "euclidean". See fastFindKNN for the dot_product restriction.
num.threads: Integer or NULL. Number of Java threads. If NULL, defaults to BiocParallel::bpworkers(BPPARAM).
BPPARAM: A BiocParallelParam object controlling the thread count. Defaults to bpparam().
ef.search: Integer. HNSW-DiskANN beam width override (0 = auto). Default 0L.
M: Integer. HNSW-DiskANN max connections per node. Default 16L.
oversample.factor: Numeric. Oversampling multiplier. Default 1.0.
pq.subspaces: Integer. PQ subspaces (0 = disabled). Default 0L.
verbose: Logical. Enable Java verbose logging. Default getOption("jvecfor.verbose", FALSE).
directed: Logical. Build directed graph? Default FALSE.
...: Additional arguments forwarded to bluster::neighborsToKNNGraph.

Value

An igraph object (KNN graph).

Examples

set.seed(42)
X <- matrix(rnorm(5000), nrow = 100, ncol = 50)

# Full examples require Java >= 20 on PATH
g <- fastMakeKNNGraph(X, k = 10)
igraph::vcount(g)  # 100
#> [1] 100