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

- (rep=1)
an

`(n-by-n)`

observation matrix, or- (rep>1)
a length-

`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 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.