This set of graph creation algorithms simulate the topology by, in some way, connecting subgraphs. The nature of their algorithm is described in detail at the linked igraph documentation.

```
play_blocks(n, size_blocks, p_between, directed = TRUE, loops = FALSE)
play_blocks_hierarchy(n, size_blocks, rho, p_within, p_between)
play_islands(n_islands, size_islands, p_within, m_between)
play_smallworld(
n_dim,
dim_size,
order,
p_rewire,
loops = FALSE,
multiple = FALSE
)
```

- n
The number of nodes in the graph.

- size_blocks
The number of vertices in each block

- p_between, p_within
The probability of edges within and between groups/blocks

- directed
Should the resulting graph be directed

- loops
Are loop edges allowed

- rho
The fraction of vertices per cluster

- n_islands
The number of densely connected islands

- size_islands
The number of nodes in each island

- m_between
The number of edges between groups/islands

- n_dim, dim_size
The dimension and size of the starting lattice

- order
The neighborhood size to create connections from

- p_rewire
The rewiring probability of edges

- multiple
Are multiple edges allowed

A tbl_graph object

`play_blocks()`

: Create graphs by sampling from stochastic block model. See`igraph::sample_sbm()`

`play_blocks_hierarchy()`

: Create graphs by sampling from the hierarchical stochastic block model. See`igraph::sample_hierarchical_sbm()`

`play_islands()`

: Create graphs with fixed size and edge probability of subgraphs as well as fixed edge count between subgraphs. See`igraph::sample_islands()`

`play_smallworld()`

: Create graphs based on the Watts-Strogatz small- world model. See`igraph::sample_smallworld()`

Other graph games:
`evolution_games`

,
`sampling_games`

,
`type_games`

```
plot(play_islands(4, 10, 0.7, 3))
```