rgraph_er function

Erdos-Renyi model

Generates a bernoulli random graph.

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.

Returns

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, c("$$", "$\n$", "$Pr{Link i-j}$"), 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.

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.

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)

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()

Author(s)

George G. Vega Yon