This games create graphs through different types of evolutionary mechanisms (not necessarily in a biological sense). The nature of their algorithm is described in detail at the linked igraph documentation.

```
play_citation_age(
n,
growth = 1,
bins = n/7100,
p_pref = (1:(bins + 1))^-3,
directed = TRUE
)
play_forestfire(
n,
p_forward,
p_backward = p_forward,
growth = 1,
directed = TRUE
)
play_growing(n, growth = 1, directed = TRUE, citation = FALSE)
play_barabasi_albert(
n,
power,
growth = 1,
growth_dist = NULL,
use_out = FALSE,
appeal_zero = 1,
directed = TRUE,
method = "psumtree"
)
play_barabasi_albert_aging(
n,
power,
power_age,
growth = 1,
growth_dist = NULL,
bins = 300,
use_out = FALSE,
appeal_zero = 1,
appeal_zero_age = 0,
directed = TRUE,
coefficient = 1,
coefficient_age = 1,
window = NULL
)
```

- n
The number of nodes in the graph.

- growth
The number of edges added at each iteration

- bins
The number of aging bins

- p_pref
The probability that an edge will be made to an age bin.

- directed
Should the resulting graph be directed

- p_forward, p_backward
Forward and backward burning probability

- citation
Should a citation graph be created

- power
The power of the preferential attachment

- growth_dist
The distribution of the number of added edges at each iteration

- use_out
Should outbound edges be used for calculating citation probability

- appeal_zero
The appeal value for unconnected nodes

- method
The algorithm to use for graph creation. Either

`'psumtree'`

,`'psumtree-multiple'`

, or`'bag'`

- power_age
The aging exponent

- appeal_zero_age
The appeal value of nodes without age

- coefficient
The coefficient of the degree dependent part of attrictiveness

- coefficient_age
The coefficient of the age dependent part of attrictiveness

- window
The aging window to take into account when calculating the preferential attraction

A tbl_graph object

`play_citation_age()`

: Create citation graphs based on a specific age link probability. See`igraph::sample_last_cit()`

`play_forestfire()`

: Create graphs by simulating the spead of fire in a forest. See`igraph::sample_forestfire()`

`play_growing()`

: Create graphs by adding a fixed number of edges at each iteration. See`igraph::sample_growing()`

`play_barabasi_albert()`

: Create graphs based on the Barabasi-Alberts preferential attachment model. See`igraph::sample_pa()`

`play_barabasi_albert_aging()`

: Create graphs based on the Barabasi-Alberts preferential attachment model, incoorporating node age preferrence. See`igraph::sample_pa_age()`

.

`play_traits()`

and `play_citation_type()`

for an evolutionary
algorithm based on different node types

Other graph games:
`component_games`

,
`sampling_games`

,
`type_games`

```
plot(play_forestfire(50, 0.5))
```