netdiffuseR (version 1.16.2)

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).

concept

Bernoulli Random graph

Erdos-Renyi random graph

Details

For each pair of nodes ${i,j}$, an edge is created with probability $p$, this is, $Pr{Link i-j} = Pr{x

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: rdiffnet, rewire_graph, rgraph_ba, rgraph_ws

Examples

# 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)