component_distribution
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.
- …
Additional attributes to pass to
cluster, right now onlymodemakes 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.
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:
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.
See Also
Examples
# NOT RUN {
g <- sample_gnp(20, 1/20)
clu <- components(g)
groups(clu)
# }