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Simulate Social Network Edge List

Source: R/simulate_edge_list.R
simulate_edge_list.Rd

Generate a simulated edge list for social network analysis. Creates random connections between nodes with weights and class assignments.

Usage

simulate_edge_list(
  n_nodes = 20,
  n_edges = NULL,
  edge_density = 3,
  n_classes = 3,
  directed = TRUE,
  allow_self_loops = FALSE,
  weight_range = c(0.1, 1),
  names = NULL,
  class_probs = NULL,
  seed = NULL
)

Arguments

n_nodes

Integer. Number of nodes (people) in the network. Default: 20.

n_edges

Integer or NULL. Number of edges to generate. If NULL, calculated as n_nodes * edge_density. Default: NULL.

edge_density

Numeric. Average number of edges per node when n_edges is NULL. Default: 3.

n_classes

Integer. Number of classes/groups (2-10). Default: 3.

directed

Logical. Whether edges are directed. Default: TRUE.

allow_self_loops

Logical. Whether to allow self-connections. Default: FALSE.

weight_range

Numeric vector of length 2. Range for edge weights. Default: c(0.1, 1.0).

names

Character vector or NULL. Custom node names. If NULL, uses names from GLOBAL_NAMES. Default: NULL.

class_probs

Numeric vector or NULL. Probability of each class. Must sum to 1 and have length n_classes. If NULL, uniform distribution. Default: NULL.

seed

Integer or NULL. Random seed for reproducibility. Default: NULL.

Value

A data frame with columns:

source

Character. Name of the source node.

target

Character. Name of the target node.

weight

Numeric. Edge weight in the specified range.

class

Integer. Class assignment (1 to n_classes).

Details

The function generates a random social network edge list with:

  • Nodes named using diverse global names (or custom names)

  • Random edges between nodes

  • Weights uniformly distributed in the specified range

  • Class assignments based on specified probabilities

For undirected networks, each edge appears once (no duplicate A-B, B-A pairs).

See also

GLOBAL_NAMES, get_global_names

Examples

# Basic usage with defaults
edges <- simulate_edge_list(seed = 42)
head(edges)
#>       source    target weight class
#> 1    Anahera  Shoshana 0.7814     1
#> 2    Anahera    Soraya 0.3799     2
#> 3     Bataar    Esther 0.7585     2
#> 4 Cuauhtemoc      Oyun 0.9325     3
#> 5 Cuauhtemoc Oyunbileg 0.4311     2
#> 6     Eloise    Esther 0.1543     1

# Larger network with 5 classes
edges <- simulate_edge_list(
  n_nodes = 50,
  n_classes = 5,
  edge_density = 4,
  seed = 123
)

# Custom names and specific number of edges
edges <- simulate_edge_list(
  n_nodes = 10,
  n_edges = 30,
  names = c("Alice", "Bob", "Carol", "Dave", "Eve",
            "Frank", "Grace", "Hank", "Ivy", "Jack"),
  n_classes = 2,
  seed = 42
)

# Undirected network with unequal class distribution
edges <- simulate_edge_list(
  n_nodes = 30,
  directed = FALSE,
  n_classes = 3,
  class_probs = c(0.5, 0.3, 0.2),
  seed = 42
)

# View class distribution
table(edges$class)
#> 
#>  1  2  3 
#> 52 21 17