Erdos-Renyi model

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Description

Generates a bernoulli random graph.

Usage

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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.

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.

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) http://barabasi.com/book/network-science.

See Also

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

Examples

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## 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)

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