netdiffuseR (version 1.17.0)

rgraph_er: Erdos-Renyi model

Description

Generates a bernoulli random graph.

Usage

rgraph_er(n = 10, t = 1, p = 0.3, undirected = getOption("diffnet.undirected"), weighted = FALSE, self = getOption("diffnet.self"), as.edgelist = FALSE)

Arguments

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.

Value

A graph represented by an adjacency matrix (if t=1), or an array of adjacency matrices (if t>1).

Details

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.

References

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

See Also

Other simulation functions: permute_graph, rdiffnet, rewire_graph, rgraph_ba, rgraph_ws, ring_lattice

Examples

## Not run: 
# # Setting the seed
# set.seed(123)
# 
# # Generating an directed graph
# rgraph_er(undirected=FALSE)
# 
# # Comparing P(tie)
# x <- rgraph_er(1000, p=.1)
# sum(x)/length(x)
# 
# # Several period random gram
# rgraph_er(t=5)
# ## End(Not run)