A vertex set is called independent if there no edges between any two vertices in it. These functions find independent vertex sets in undirected graphs

## Arguments

- graph
The input graph, directed graphs are considered as undirected, loop edges and multiple edges are ignored.

- min
Numeric constant, limit for the minimum size of the independent vertex sets to find.

`NULL`

means no limit.- max
Numeric constant, limit for the maximum size of the independent vertex sets to find.

`NULL`

means no limit.

## Value

`ivs()`

,
`largest_ivs()`

and
`maximal_ivs()`

return a list containing numeric
vertex ids, each list element is an independent vertex set.

`ivs_size()`

returns an integer constant.

## Details

`ivs()`

finds all independent vertex sets in the
network, obeying the size limitations given in the `min`

and `max`

arguments.

`largest_ivs()`

finds the largest independent vertex
sets in the graph. An independent vertex set is largest if there is no
independent vertex set with more vertices.

`maximal_ivs()`

finds the maximal independent vertex
sets in the graph. An independent vertex set is maximal if it cannot be
extended to a larger independent vertex set. The largest independent vertex
sets are maximal, but the opposite is not always true.

`independece.number`

calculate the size of the largest independent
vertex set(s).

These functions use the algorithm described by Tsukiyama et al., see reference below.

## References

S. Tsukiyama, M. Ide, H. Ariyoshi and I. Shirawaka. A new
algorithm for generating all the maximal independent sets. *SIAM J
Computing*, 6:505--517, 1977.

## See also

Other cliques:
`cliques()`

,
`weighted_cliques()`

## Author

Tamas Nepusz ntamas@gmail.com ported it from the Very Nauty Graph Library by Keith Briggs (http://keithbriggs.info/) and Gabor Csardi csardi.gabor@gmail.com wrote the R interface and this manual page.

## Examples

```
# Do not run, takes a couple of seconds
# A quite dense graph
set.seed(42)
g <- sample_gnp(100, 0.9)
ivs_size(g)
#> [1] 4
ivs(g, min = ivs_size(g))
#> [[1]]
#> + 4/100 vertices, from deea9ae:
#> [1] 7 37 55 56
#>
#> [[2]]
#> + 4/100 vertices, from deea9ae:
#> [1] 7 55 56 69
#>
#> [[3]]
#> + 4/100 vertices, from deea9ae:
#> [1] 7 56 69 74
#>
#> [[4]]
#> + 4/100 vertices, from deea9ae:
#> [1] 8 15 73 80
#>
#> [[5]]
#> + 4/100 vertices, from deea9ae:
#> [1] 8 15 73 84
#>
#> [[6]]
#> + 4/100 vertices, from deea9ae:
#> [1] 13 16 37 40
#>
#> [[7]]
#> + 4/100 vertices, from deea9ae:
#> [1] 21 32 45 61
#>
#> [[8]]
#> + 4/100 vertices, from deea9ae:
#> [1] 22 55 56 64
#>
#> [[9]]
#> + 4/100 vertices, from deea9ae:
#> [1] 23 69 75 90
#>
largest_ivs(g)
#> [[1]]
#> + 4/100 vertices, from deea9ae:
#> [1] 21 32 45 61
#>
#> [[2]]
#> + 4/100 vertices, from deea9ae:
#> [1] 7 37 55 56
#>
#> [[3]]
#> + 4/100 vertices, from deea9ae:
#> [1] 7 55 56 69
#>
#> [[4]]
#> + 4/100 vertices, from deea9ae:
#> [1] 7 56 69 74
#>
#> [[5]]
#> + 4/100 vertices, from deea9ae:
#> [1] 8 15 73 80
#>
#> [[6]]
#> + 4/100 vertices, from deea9ae:
#> [1] 8 15 73 84
#>
#> [[7]]
#> + 4/100 vertices, from deea9ae:
#> [1] 22 55 56 64
#>
#> [[8]]
#> + 4/100 vertices, from deea9ae:
#> [1] 23 69 75 90
#>
#> [[9]]
#> + 4/100 vertices, from deea9ae:
#> [1] 13 16 37 40
#>
# Empty graph
induced_subgraph(g, largest_ivs(g)[[1]])
#> IGRAPH 2246453 U--- 4 0 -- Erdos-Renyi (gnp) graph
#> + attr: name (g/c), type (g/c), loops (g/l), p (g/n)
#> + edges from 2246453:
length(maximal_ivs(g))
#> [1] 326
```