Description Usage Arguments Details Value References Examples
Erdos-Renyi random graph model is one of the most popular and
fundamental examples in modeling networks. Given n nodes,
gmodel.ER
generates edges randomly from Bernoulli distribution
with a globally specified probability.
1 |
n |
the number of nodes to be generated |
mode |
'prob' (default) for edges to be drawn from Bernoulli distribution independently, or 'num' for a graph to have a fixed number of edges placed randomly |
par |
a real number \in [0,1] for |
rep |
the number of observations to be generated. |
In network science, 'ER' model is often interchangeably used in where we have fixed number of edges to be placed at random. The original use of edge-generating probability is from Gilbert (1959). Therefore, we set this algorithm to be flexible in that user can create either a fixed number of edges placed at random or set global edge-generating probability and draw independent observations following Bernoulli distribution.
depending on rep
value, either
an (n\times n) observation matrix, or
a length-rep
list where each element
is an observation is an (n\times n) realization from the model.
Erdos1959graphon
\insertRefGilbert1959graphon
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