# sample_growing

From igraph v1.2.5
by Gabor Csardi

##### Growing random graph generation

This function creates a random graph by simulating its stochastic evolution.

- Keywords
- graphs

##### Usage

`sample_growing(n, m = 1, directed = TRUE, citation = FALSE)`growing(...)

##### Arguments

- n
Numeric constant, number of vertices in the graph.

- m
Numeric constant, number of edges added in each time step.

- directed
Logical, whether to create a directed graph.

- citation
Logical. If

`TRUE`

a citation graph is created, ie. in each time step the added edges are originating from the new vertex.- ...
Passed to

`sample_app`

.

##### Details

This is discrete time step model, in each time step a new vertex is added to
the graph and `m`

new edges are created. If `citation`

is
`FALSE`

these edges are connecting two uniformly randomly chosen
vertices, otherwise the edges are connecting new vertex to uniformly
randomly chosen old vertices.

##### Value

A new graph object.

##### See Also

##### Examples

```
# NOT RUN {
g <- sample_growing(500, citation=FALSE)
g2 <- sample_growing(500, citation=TRUE)
# }
```

*Documentation reproduced from package igraph, version 1.2.5, License: GPL (>= 2)*

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