directed_erdos_renyi {fastRG}

Create an directed erdos renyi object

Description

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() object.

• S: The mixing matrix as a Matrix() object.

• Y: The outgoing latent positions as a 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.

See Also

Other erdos renyi: erdos_renyi()

Other directed graphs: directed_dcsbm()

Examples

set.seed(87)

er <- directed_erdos_renyi(n = 10, p = 0.1)
er

big_er <- directed_erdos_renyi(n = 10^6, expected_in_degree = 5)
big_er

A <- sample_sparse(er)
A