# component_distribution

0th

Percentile

##### 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 or strong. For directed graphs weak 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:

• 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 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.

subcomponent, groups

##### Aliases
• cluster.distribution
• clusters
• component_distribution
• components
• count_components
• is.connected
• is_connected
• no.clusters
##### Examples
g <- sample_gnp(20, 1/20)
clu <- components(g)
groups(clu)
Documentation reproduced from package igraph, version 1.0.0, License: GPL (>= 2)

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