Generates a bernoulli random graph.

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`n` |
Integer. Number of vertices |

`t` |
Integer. Number of time periods |

`p` |
Double. Probability of a link between ego and alter. |

`undirected` |
Logical scalar. Whether the graph is undirected or not. |

`weighted` |
Logical. Whether the graph is weighted or not. |

`self` |
Logical. Whether it includes self-edges. |

`as.edgelist` |
Logical. When TRUE the graph is presented as an edgelist instead of an adjacency matrix. |

For each pair of nodes *{i,j}*, an edge is created
with probability *p*, this is, *
Pr{Link i-j}*, where *x* is drawn from a *Uniform(0,1)*.

When `weighted=TRUE`

, the strength of ties is given by
the random draw *x* used to compare against *p*, hence, if *x < p*
then the strength will be set to *x*.

In the case of dynamic graphs, the algorithm is repeated *t* times, so the
networks are uncorrelated.

A graph represented by an adjacency matrix (if `t=1`

), or an array of
adjacency matrices (if `t>1`

).

The resulting adjacency matrix is store as a dense matrix, not as a sparse matrix, hence the user should be careful when choosing the size of the network.

George G. Vega Yon

Barabasi, Albert-Laszlo. "Network science book" Retrieved November 1 (2015) http://barabasi.com/book/network-science.

Other simulation functions: `permute_graph`

,
`rdiffnet`

, `rewire_graph`

,
`rgraph_ba`

, `rgraph_ws`

,
`ring_lattice`

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Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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