simulate_network: Simulate a network from common graph models

In simulab: Simulates a Wide Variety of Data Shapes, Sizes and Distributions

Simulate a network from common graph models

Description

Simulate a network from common graph models

Usage

simulate_network(
  nodes,
  model = c("bernoulli", "barabasi_albert", "small_world", "block", "regular",
    "geometric", "forest_fire"),
  probability = 0.1,
  edges = NULL,
  directed = TRUE,
  loops = FALSE,
  weight = c("binary", "uniform", "normal", "poisson"),
  weight_mean = 1,
  weight_sd = 1,
  weight_range = c(0.1, 1),
  node_type = NULL,
  edge_classes = NULL,
  class_probabilities = NULL,
  attachment = 2L,
  power = 1,
  neighbors = 2L,
  rewire = 0.05,
  blocks = 3L,
  within_probability = 0.3,
  between_probability = 0.05,
  degree = 4L,
  radius = 0.25,
  forward_probability = 0.35,
  backward_probability = 0.32,
  seed = NULL,
  batch = NULL
)

Arguments

nodes	Number of nodes or node labels. A single number produces the integer labels 1:nodes.
model	Graph model. Every model except bernoulli requires the suggested igraph package.
probability	Bernoulli edge probability, node matrix, or type matrix. A matrix may instead be given as a tidy data frame with columns from, to and probability. Used by the bernoulli model only; the block model uses within_probability and between_probability instead.
edges	Exact edge count for the fixed-edge Bernoulli model, capped at the number of admissible dyads.
directed	Generate directed edges. Ignored by the small_world and geometric models, which are always undirected; the directed column of the settings table reports what was actually generated.
loops	Permit self-loops.
weight	Edge-weight distribution.
weight_mean, weight_sd, weight_range	Weight parameters.
node_type	Optional type label for each node. With more than one type it also supplies the block membership used by the block model.
edge_classes	Optional number or labels of edge classes.
class_probabilities	Optional edge-class probabilities.
attachment, power	Preferential-attachment parameters.
neighbors, rewire	Small-world parameters.
blocks, within_probability, between_probability	Block-model parameters.
degree	Regular-graph degree.
radius	Geometric-graph connection radius.
forward_probability, backward_probability	Forest-fire parameters.
seed	Optional random seed.
batch	Optional single positive whole number. When given, the simulator runs batch times and returns a plain list of batch results, each exactly what the same call without batch returns. With a seed, every dataset gets its own seed drawn from seed, so the whole batch is reproducible; without one the datasets are consecutive draws from the session's random-number stream. The default NULL returns a single result.

Value

A tidy edge-list simulab_sim base data.frame with one row per edge and columns from, to, weight (plus edge_class when edge_classes is supplied). as.data.frame(x, what = ) also returns nodes (node, type), adjacency (a long row/column/weight table covering all nodes^2 ordered pairs, symmetric when the generated graph is undirected), and a one-row settings table. Use as_igraph() for native graph workflows.

Examples

result <- simulate_network(
  nodes = 60, model = "bernoulli", probability = 0.06, seed = 1
)
head(result)
components(result)

# Other generators: barabasi_albert, small_world, block, regular,
# geometric and forest_fire. These require the suggested igraph package.
if (requireNamespace("igraph", quietly = TRUE)) {
  head(simulate_network(nodes = 60, model = "small_world", neighbors = 2, seed = 1))
}

simulab documentation built on Oct. 15, 2026, 5:08 p.m.