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 |

`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

- (rep=1)
an

*(n\times n)*observation matrix, or- (rep>1)
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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