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.
the number of nodes to be generated
'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
a real number in [0,1] for
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.
rep value, either
(n-by-n) observation matrix, or
rep list where each element
is an observation is an
(n-by-n) realization from the model.
Erdos, P. and Renyi, A. (1959) On Random Graphs I. Publications Mathematicae, Vol.6:290-297.
Gilbert, E.N. (1959) Random Graphs. Annals of Mathematical Statistics, Vol.30, No.4:1141-1144.
1 2 3 4 5 6 7 8 9 10 11
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.