Simulate a multiplex network

Usage

simulate_multiplex_network(
  nodes,
  layers,
  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,
  seed = NULL,
  batch = NULL
)

Arguments

nodes: Number of nodes or unique labels shared across layers.
layers: Number of layers or unique layer labels.
edges: Optional exact edge count, scalar or one value per layer.
probability: Edge probability, scalar or one value per layer.
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.
seed: Optional base seed; layers use deterministic offsets.
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 layered edge-list simulab_sim base data.frame with one row per edge and columns layer, from, to, and weight. Every layer is drawn independently over the same node set. as.data.frame(x, what = ) also returns nodes (node, type), layers (layer, probability), adjacency (a long layer/row/column/weight table over all ordered node pairs in every layer), and a one-row settings table.

Examples

result <- simulate_multiplex_network(nodes = 25, layers = 3, probability = 0.1, seed = 1)
head(result)
#> <simulab_sim:multiplex_network> 6 rows x 4 columns
#>     layer    from     to weight
#> 1 Layer 1  Node 5 Node 1      1
#> 2 Layer 1  Node 8 Node 1      1
#> 3 Layer 1 Node 19 Node 1      1
#> 4 Layer 1 Node 22 Node 1      1
#> 5 Layer 1 Node 14 Node 3      1
#> 6 Layer 1  Node 9 Node 4      1
#> 
#> Truth (settings):
#>   layers directed loops weight
#> 1      3     TRUE FALSE binary
#> 
#> Other tables: nodes, layers, adjacency. Read one with as.data.frame(x, what = "nodes").
components(result)
#>       table rows columns
#> 1      data  182       4
#> 2     nodes   25       2
#> 3    layers    3       2
#> 4 adjacency 1875       4
#> 5  settings    1       4