qpgraph (version 2.6.1)

qpCliqueNumber: Clique number

Description

Calculates the size of the largest maximal clique (the so-called clique number or maximum clique size) in a given undirected graph.

Usage

qpCliqueNumber(g, exact.calculation=TRUE, return.vertices=FALSE, approx.iter=100, verbose=TRUE, R.code.only)

Arguments

g

either a graphNEL object or an adjacency matrix of the given undirected graph.

exact.calculation

logical; if TRUE then the exact clique number is calculated; if FALSE then a lower bound is given instead.

return.vertices

logical; if TRUE a set of vertices forming a maximal clique of maximum size is returned; if FALSE only the maximum clique size is returned.

approx.iter

number of iterations to be employed in the calculation of the lower bound (i.e., only applies when exact.calculation=FALSE.

verbose

show progress on calculations.

R.code.only

logical; if FALSE then the faster C implementation is used (default); if TRUE then only R code is executed.

Value

a lower bound of the size of the largest maximal clique in the given graph, also known as its clique number.

Details

The calculation of the clique number of an undirected graph is one of the basic NP-complete problems (Karp, 1972) which means that its computational cost is bounded by an exponential running time (Pardalos and Xue, 1994). The current implementation uses C code from the GNU GPL Cliquer library by Niskanen and Ostergard (2003) based on the, probably the fastest to date, algorithm by Ostergard (2002).

The lower bound on the maximum clique size is calculated by ranking the vertices by their connectivity degree, put the first vertex in a set and go through the rest of the ranking adding those vertices to the set that form a clique with the vertices currently within the set. Once the entire ranking has been examined a large clique should have been built and eventually one of the largests ones. This process is repeated a number of times (approx.iter) each of which the ranking is altered with increasing levels of randomness acyclically (altering 1 to $p$ vertices and again). Larger values of approx.iter should provide tighter lower bounds although it has been proven that no polynomial time algorithm can approximate the maximum clique size within a factor of $n^\epsilon$ ($\epsilon > 0$), unless P=NP (Feige et al, 1991; Pardalos and Xue, 1994).

References

Castelo, R. and Roverato, A. A robust procedure for Gaussian graphical model search from microarray data with p larger than n. J. Mach. Learn. Res., 7:2621-2650, 2006.

Feige, U., Goldwasser, S., Lov\'asz, L., Safra, S. and Szegedy, M. Approximating the maximum clique is almost NP-Complete. Proc. 32nd IEEE Symp. on Foundations of Computer Science, 2-12, 1991.

Karp, R.M. Reducibility among combinatorial problems. Complexity of computer computations, 43:85-103, 1972.

Niskanen, S. Ostergard, P. Cliquer User's Guide, Version 1.0. Communications Laboratory, Helsinki University of Technology, Espoo, Finland, Tech. Rep. T48, 2003. (http://users.tkk.fi/~pat/cliquer.html)

Ostergard, P. A fast algorithm for the maximum clique problem. Discrete Appl. Math. 120:197-207, 2002.

Pardalos, P.M. and Xue, J. The maximum clique problem. J. Global Optim., 4:301-328, 1994.

See Also

qpClique

Examples

require(graph)

nVar <- 50

set.seed(123)

g1 <- randomEGraph(V=as.character(1:nVar), p=0.3)
qpCliqueNumber(g1, verbose=FALSE)

g2 <- randomEGraph(V=as.character(1:nVar), p=0.7)
qpCliqueNumber(g2, verbose=FALSE)