# component_distribution

##### Connected components of a graph

Calculate the maximal (weakly or strongly) connected components of a graph

- Keywords
- graphs

##### Usage

`component_distribution(graph, cumulative = FALSE, mul.size = FALSE, ...)`components(graph, mode = c("weak", "strong"))

##### Arguments

- graph
- The graph to analyze.
- cumulative
- Logical, if TRUE the cumulative distirubution (relative frequency) is calculated.
- mul.size
- Logical. If TRUE the relative frequencies will be multiplied by the cluster sizes.
- mode
- Character string, either
weak orstrong . For directed graphsweak implies weakly,strong strongly connected components to search. It is ignored for undirected graphs. - ...
- Additional attributes to pass to
`cluster`

, right now only`mode`

makes sense.

##### Details

`is_connected`

decides whether the graph is weakly or strongly
connected.

`components`

finds the maximal (weakly or strongly) connected components
of a graph.

`count_components`

does almost the same as `components`

but returns only
the number of clusters found instead of returning the actual clusters.

`component_distribution`

creates a histogram for the maximal connected
component sizes.

The weakly connected components are found by a simple breadth-first search. The strongly connected components are implemented by two consecutive depth-first searches.

##### Value

- For
`is_connected`

a logical constant.For

`components`

a named list with three components: membership numeric vector giving the cluster id to which each vertex belongs. csize numeric vector giving the sizes of the clusters. no numeric constant, the number of clusters. - For
`count_components`

an integer constant is returned.For

`component_distribution`

a numeric vector with the relative frequencies. The length of the vector is the size of the largest component plus one. Note that (for currently unknown reasons) the first element of the vector is the number of clusters of size zero, so this is always zero.

##### See Also

##### Examples

```
g <- sample_gnp(20, 1/20)
clu <- components(g)
groups(clu)
```

*Documentation reproduced from package igraph, version 1.0.0, License: GPL (>= 2)*