rgraph_er: Erdos-Renyi model

In netdiffuseR: Analysis of Diffusion and Contagion Processes on Networks

Erdos-Renyi model

Description

Generates a bernoulli random graph.

Usage

rgraph_er(
  n = 10,
  t = 1,
  p = 0.01,
  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.

Details

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

Value

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

Note

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.

Author(s)

George G. Vega Yon

References

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

See Also

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

Examples

# Setting the seed
set.seed(13)

# Generating an directed graph
rgraph_er(undirected=FALSE, p = 0.1)

# Comparing P(tie)
x <- rgraph_er(1000, p=.1)
sum(x)/length(x)

# Several period random gram
rgraph_er(t=5)

netdiffuseR documentation built on Aug. 30, 2023, 5:07 p.m.