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

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

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.

…

Additional attributes to pass to `cluster`

, right now only
`mode`

makes sense.

mode

Character string, either “weak” or “strong”. For directed graphs “weak” implies weakly, “strong” strongly connected components to search. It is ignored for undirected graphs.

For `is_connected`

a logical constant.

For `components`

a named list with three components:

numeric vector giving the cluster id to which each vertex belongs.

numeric vector giving the sizes of the clusters.

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.

`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.

```
# NOT RUN {
g <- sample_gnp(20, 1/20)
clu <- components(g)
groups(clu)
# }
```

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