rgraph_er: Erdos-Renyi model In netdiffuseR: Analysis of Diffusion and Contagion Processes on Networks

Description

Generates a bernoulli random graph.

Usage

 ```1 2 3``` ```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}, 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.

Other simulation functions: `permute_graph`, `rdiffnet`, `rewire_graph`, `rgraph_ba`, `rgraph_ws`, `ring_lattice`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```## 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) ```