clusters {igraph} R Documentation

## Connected components of a graph

### Description

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

### Usage

```is.connected(graph, mode=c("weak", "strong"))
clusters(graph, mode=c("weak", "strong"))
no.clusters(graph, mode=c("weak", "strong"))
cluster.distribution(graph, cumulative = FALSE, mul.size = FALSE, ...)
```

### Arguments

 `graph` The graph to analyze. `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. `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.

### Details

`is.connected` decides whether the graph is weakly or strongly connected.

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

`no.clusters` does almost the same as `clusters` but returns only the number of clusters found instead of returning the actual clusters.

`cluster.distribution` creates a histogram for the maximal connected component sizes.

Breadth-first search is conducted from each not-yet visited vertex.

### Value

For `is.connected` a logical constant.
For `clusters` 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.

normal-bracket42bracket-normal
For `no.clusters` an integer constant is returned.
For `cluster.distribution` a numeric vector with the relative frequencies. The length of the vector is the number of components.

### Author(s)

Gabor Csardi csardi@rmki.kfki.hu

`subcomponent`
```g <- erdos.renyi.game(20, 1/20)