Getting Started with netOP

Generate and inspect a network

Generators return adjacency matrices directly and attach only compact truth metadata. This keeps sparse output useful without attaching a dense probability matrix.

A <- generate_sbm(
  n = 200,
  K = 3,
  alpha = 0.5,
  beta = 0.08,
  representation = "dense",
  seed = 1,
  ncores = 1
)
parameters <- get_generator_parameters(A)
table(parameters$g_true)

## 
##  1  2  3 
## 66 56 78

Sparse output is the default where a generator supports it. netOP re-exports the Matrix-aware mean(), sum(), diag(), rowMeans(), rowSums(), colMeans(), and colSums() generics, so common summaries work after loading netOP without separately attaching Matrix. Choose representation = "dense" only when downstream software requires an ordinary dense matrix.

Embed and cluster

embedding <- ase(A, d = 3)

## n <= 200; using engine = 'base'.

dim(embedding$Z_hat)

## [1] 200   3

clustering <- spectral_cluster(
  A,
  K = 3,
  spectral_engine = "base",
  cluster_engine = "kmeans"
)
table(clustering$g_hat)

## 
##  1  2  3 
## 66 56 78

Select a model

The public model-selection APIs begin with the network and candidate set. Examples use one worker and small deterministic inputs; production analyses can increase repetition counts and choose partial eigensolvers.

selection <- netcrop_blockmodel(
  A,
  K_candidates = 1:5,
  num_subnetworks = 2,
  overlap_size = 50,
  nrep = 1,
  losses = "sse",
  ncores = 1,
  seed = 2,
  verbose = FALSE,
  sbm_est_options = list(spectral_cluster = list(spectral_engine = "base")),
  dcbm_est_options = list(spectral_cluster = list(spectral_engine = "base"))
)
selection$best_model_overall

Setting seed makes randomized stages reproducible. The examples use ncores = 1 because that is portable across operating systems and keeps the vignette deterministic. For larger analyses, supported routines can use more workers; consult each function’s seed documentation for its parallel reproducibility contract.

NETCROP is also available for RDPG and latent-space dimensions and spectral regularization. The self-contained ECV and NCV wrappers provide alternative block-model stability selectors; see ?ecv_stability_blockmodel and ?ncv_stability_blockmodel for disclosures, algorithm restrictions, and citations.

See the choosing-a-method article for a side-by-side guide to NETCROP, ECV, NCV, DKEST, and SONNET.

Results and plotting

All high-level model-selection results provide print() and summary() methods. Plotting is available when ggplot2 is installed.

summary(selection)
if (requireNamespace("ggplot2", quietly = TRUE)) {
  plot(selection)
}