cliques(graph, min = NULL, max = NULL)max_cliques(graph, min = NULL, max = NULL, subset = NULL, file = NULL)
NULL
means no limit, ie. it is the same as 0.NULL
means no limit.NULL
, then it must be a vector of vertex ids,
numeric or symbolic if the graph is named. The algorithm is run from these
vertices only, so only a subset of all maximal cliques is returned. See the
Eppstein paper for details. This argumNULL
, then it must be a file name, i.e. a
character scalar. The output of the algorithm is written to this file. (If
it exists, then it will be overwritten.) Each clique will be a separate line
in the file, given with the numeric ids ocliques
, largest_cliques
and clique_num
return a list containing numeric vectors of vertex ids. Each list element is
a clique.max_cliques
returns NULL
, invisibly, if its file
argument is not NULL
. The output is written to the specified file in
this case.
clique_num
and count_max_cliques
return an integer
scalar.
cliques
find all complete subgraphs in the input graph, obeying the
size limitations given in the min
and max
arguments.largest_cliques
finds all largest cliques in the input graph. A
clique is largest if there is no other clique including more vertices.
max_cliques
finds all maximal cliques in the input graph. A
clique in maximal if it cannot be extended to a larger clique. The largest
cliques are always maximal, but a maximal clique is not neccessarily the
largest.
count_max_cliques
counts the maximal cliques.
clique_num
calculates the size of the largest clique(s).
The current implementation of these functions searches for maximal
independent vertex sets (see ivs
) in the
complementer graph.
ivs
# this usually contains cliques of size six
g <- sample_gnp(100, 0.3)
clique_num(g)
cliques(g, min=6)
largest_cliques(g)
# To have a bit less maximal cliques, about 100-200 usually
g <- sample_gnp(100, 0.03)
max_cliques(g)
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