An undirected_factor_model S3 class based on a list
with the following elements:
X: The latent positions as a Matrix() object.
S: The mixing matrix as a Matrix() object.
n: The number of nodes in the network.
k: The rank of expectation matrix. Equivalently,
the dimension of the latent node position vectors.
Arguments
n
Number of nodes in graph.
...
Arguments passed on to undirected_factor_model
expected_degree
If specified, the desired expected degree
of the graph. Specifying expected_degree simply rescales S
to achieve this. Defaults to NULL. Do not specify both
expected_degree and expected_density at the same time.
p
Probability of an edge between any two nodes. You must specify
either p or expected_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.
See Also
Other erdos renyi:
directed_erdos_renyi()
Other undirected graphs:
chung_lu(),
dcsbm(),
mmsbm(),
overlapping_sbm(),
planted_partition(),
sbm()
set.seed(87)
er <- erdos_renyi(n = 10, p = 0.1)
er
er <- erdos_renyi(n = 10, expected_density = 0.1)
er
big_er <- erdos_renyi(n = 10^6, expected_degree = 5)
big_er
A <- sample_sparse(er)
A