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:
    • membership
    {numeric vector giving the cluster id to which each vertex belongs.}
  • csizenumeric vector giving the sizes of the clusters.
  • nonumeric constant, the number of clusters.

concept

  • Connectedness
  • Graph component

code

cluster.distribution

See Also

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 0.5.1, License: GPL (>= 2)

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