Simulate a tidy network edge list

Usage

simulate_edge_list(
  nodes,
  edges = NULL,
  probability = 0.1,
  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,
  seed = NULL,
  batch = NULL
)

Arguments

nodes: Number of nodes or unique node labels.
edges: Optional exact number of edges.
probability: Edge probability when edges is NULL.
directed: Generate directed edges.
loops: Permit self-loops.
weight: Edge-weight distribution.
weight_mean, weight_sd, weight_range: Weight parameters.
node_type: Optional node type for each node.
edge_classes: Optional number or labels of edge classes.
class_probabilities: Optional edge-class probabilities.
seed: Optional 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 simulab_sim base data.frame with one row per edge and columns from, to, and 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 over all ordered node pairs), and a one-row settings table.

Examples

result <- simulate_edge_list(nodes = 40, probability = 0.08, seed = 1)
head(result)
#> <simulab_sim:edge_list> 6 rows x 3 columns
#>   from to weight
#> 1    8  1      1
#> 2   19  1      1
#> 3   22  1      1
#> 4    2  3      1
#> 5   27  3      1
#> 6   32  3      1
#> 
#> Truth (settings):
#>       model directed loops weight nodes edges
#> 1 bernoulli     TRUE FALSE binary    40   138
#> 
#> Other tables: nodes, adjacency. Read one with as.data.frame(x, what = "nodes").
components(result)
#>       table rows columns
#> 1      data  138       3
#> 2     nodes   40       2
#> 3 adjacency 1600       3
#> 4  settings    1       6