# clusters

0th

Percentile

##### Connected components of a graph

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

Keywords
graphs
##### 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:
• membershipnumeric vector giving the cluster id to which each vertex belongs.
• csizenumeric vector giving the sizes of the clusters.
• nonumeric constant, the number of clusters.
• 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 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.

##### concept

• Connectedness
• Graph component

subcomponent

##### Aliases
• no.clusters
• clusters
• is.connected
• cluster.distribution
##### Examples
g <- erdos.renyi.game(20, 1/20)
clusters(g)
Documentation reproduced from package igraph, version 0.5.3, License: GPL (>= 2)

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