Create an directed erdos renyi object

Usage

directed_erdos_renyi(
  n,
  ...,
  p = NULL,
  poisson_edges = TRUE,
  allow_self_loops = TRUE
)

Arguments

n

Number of nodes in graph.

...

Arguments passed on to directed_factor_model

expected_in_degree: If specified, the desired expected in degree of the graph. Specifying expected_in_degree simply rescales S to achieve this. Defaults to NULL. Specify only one of expected_in_degree, expected_out_degree, and expected_density.
expected_out_degree: If specified, the desired expected out degree of the graph. Specifying expected_out_degree simply rescales S to achieve this. Defaults to NULL. Specify only one of expected_in_degree, expected_out_degree, and expected_density.

p

Probability of an edge between any two nodes. You must specify either p, expected_in_degree, or expected_out_degree.

poisson_edges

Logical indicating whether or not multiple edges are allowed to form between a pair of nodes. Defaults to TRUE. When FALSE, sampling proceeds as usual, and duplicate edges are removed afterwards. Further, when FALSE, we assume that S specifies a desired between-factor connection probability, and back-transform this S to the appropriate Poisson intensity parameter to approximate Bernoulli factor connection probabilities. See Section 2.3 of Rohe et al. (2017) for some additional details.

allow_self_loops

Logical indicating whether or not nodes should be allowed to form edges with themselves. Defaults to TRUE. When FALSE, sampling proceeds allowing self-loops, and these are then removed after the fact.

Value

A directed_factor_model S3 class based on a list with the following elements:

X: The incoming latent positions as a Matrix::Matrix() object.
S: The mixing matrix as a Matrix::Matrix() object.
Y: The outgoing latent positions as a Matrix::Matrix() object.
n: The number of nodes with incoming edges in the network.
k1: The dimension of the latent node position vectors encoding incoming latent communities (i.e. in X).
d: The number of nodes with outgoing edges in the network. Does not need to match n – rectangular adjacency matrices are supported.
k2: The dimension of the latent node position vectors encoding outgoing latent communities (i.e. in Y).
poisson_edges: Whether or not the graph is taken to be have Poisson or Bernoulli edges, as indicated by a logical vector of length 1.
allow_self_loops: Whether or not self loops are allowed.

Examples


set.seed(87)

er <- directed_erdos_renyi(n = 10, p = 0.1)
er
#> Directed Factor Model
#> ---------------------
#> 
#> Incoming Nodes (n): 10
#> Incoming Rank (k1): 1
#> Outgoing Rank (k2): 1
#> Outgoing Nodes (d): 10
#> 
#> X: 10 x 1 [dgeMatrix] 
#> S: 1 x 1 [ddiMatrix] 
#> Y: 10 x 1 [dgeMatrix] 
#> 
#> Poisson edges: TRUE 
#> Allow self loops: TRUE 
#> 
#> Expected edges: 10
#> Expected density: 0.1
#> Expected in degree: 1
#> Expected out degree: 1

big_er <- directed_erdos_renyi(n = 1000, expected_in_degree = 5)
big_er
#> Directed Factor Model
#> ---------------------
#> 
#> Incoming Nodes (n): 1000
#> Incoming Rank (k1): 1
#> Outgoing Rank (k2): 1
#> Outgoing Nodes (d): 1000
#> 
#> X: 1000 x 1 [dgeMatrix] 
#> S: 1 x 1 [ddiMatrix] 
#> Y: 1000 x 1 [dgeMatrix] 
#> 
#> Poisson edges: TRUE 
#> Allow self loops: TRUE 
#> 
#> Expected edges: 5000
#> Expected density: 0.005
#> Expected in degree: 5
#> Expected out degree: 5

A <- sample_sparse(er)
A
#> 10 x 10 sparse Matrix of class "dgCMatrix"
#>                          
#>  [1,] . . . . . . . . . .
#>  [2,] . . . . . . 1 . . .
#>  [3,] . . . 1 . . . . . .
#>  [4,] . . . . 1 . 1 . 1 1
#>  [5,] . . . . . . . . . .
#>  [6,] . . . . . . 1 . . .
#>  [7,] . . . . . . . . . .
#>  [8,] 1 . . . . . . . . .
#>  [9,] . . . . . 1 . . . .
#> [10,] . . . . . . 2 . . .

Create an directed erdos renyi object

Usage

Arguments

Value

See also

Examples