graph generation utilities {bnlearn} | R Documentation

## Generate empty or random graphs

### Description

Generate empty or random directed acyclic graphs from a given set of nodes.

### Usage

    empty.graph(nodes, num = 1)
    random.graph(nodes, num = 1, method = "ordered", ..., debug = FALSE)

### Arguments

`nodes` | a vector of character strings, the labels of the nodes.
`num` | an integer, the number of graphs to be generated.
`method` | a character string, the label of a score. Possible values are `ordered` (full ordering based generation), `ic-dag` (Ide's and Cozman's Generating Multi-connected DAGs algorithm), `melancon` (Melancon's and Philippe's Uniform Random Acyclic Digraphs algorithm) and `empty` (generates empty graphs).
`...` | additional tuning parameters (see below).
`debug` | a boolean value. If `TRUE` a lot of debugging output is printed; otherwise the function is completely silent. Ignored in some generation methods.

### Details

Available graph generation algorithms are:

- full ordering based generation (`ordered`): generates graphs whose node ordering is given by the order of the labels in the `nodes` argument. The same algorithm is used in the `randomDAG` function in package pcalg.

- Ide's and Cozman's Generating Multi-connected DAGs algorithm (`ic-dag`): generates graphs with a uniform probability distribution over the set of multiconnected graphs.

- Melancon's and Philippe's Uniform Random Acyclic Digraphs algorithm (`melancon`): generates graphs with a uniform probability distribution over the set of all possible graphs.

- empty graphs (`empty`): generates graphs without any arc.

Additional arguments for the `random.graph` function are:

- `prob`: the probability of each arc to be present in a graph generated by the `ordered` algorithm. The default value is `2 / (length(nodes) - 1)`, which results in a sparse graph (the number of arcs should be of the same order as the number of nodes).

- `burn.in`: the number of iterations for the `ic-dag` and `melancon` algorithms to converge to a stationary (and uniform) probability distribution. The default value is `6 * length(nodes)^2`.

- `every`: return only one graph `every` number of steps instead of all the graphs generated with `ic-dag` and `melancon`. Since both algorithms are based on Markov Chain Monte Carlo approaches, high values of `every` result in a more diverse set of networks. The default value is `1`, i.e. to return all the networks that are generated.

- `max.degree`: the maximum degree for any node in a graph generated by the `ic-dag` and `melancon` algorithms. The default value is `Inf`.

- `max.in.degree`: the maximum in-degree for any node in a graph generated by the `ic-dag` and `melancon` algorithms. The default value is `Inf`.

- `max.out.degree`: the maximum out-degree for any node in a graph generated by the `ic-dag` and `melancon` algorithms. The default value is `Inf`.

### Value

Both `empty.graph()` and `random.graph()` return an object of class `bn` (if `num` is equal to `1`) or a list of objects of class `bn` (otherwise). If `every` is greated than `1`, `random.graph` always returns a list, regardless of the number of graphs it contains.

### Author(s)

Marco Scutari

### References

Ide JS, Cozman FG (2002). "Random Generation of Bayesian Networks". Proceedings of the 16th Brazilian Symposium on Artificial Intelligence, 366–375.

Melancon G, Dutour I, Bousquet-Melou M (2001). "Random Generation of Directed Acyclic Graphs". Electronic Notes in Discrete Mathematics, 10:202–207.

Melancon G, Philippe F (2004). "Generating Connected Acyclic Digraphs Uniformly at Random". Information Processing Letters, 90(4):209–213.

### Examples

    empty.graph(LETTERS[1:8])
    random.graph(LETTERS[1:8])
    plot(random.graph(LETTERS[1:8], method = "ic-dag", max.in.degree = 2))
    plot(random.graph(LETTERS[1:8]))
    plot(random.graph(LETTERS[1:8], prob = 0.2))

[Package bnlearn version 4.7.1 Index]