scgExtra

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SCG Extra Functions

Some useful functions to perform general actions in Spectral Coarse Graining (SCG).

Keywords
graphs
Usage
scgNormEps(V, groups, mtype = c("symmetric", "laplacian",
          "stochastic"), p = NULL, norm = c("row", "col"))
Arguments
V
A numeric matrix of (eigen)vectors assumed normalized. The vectors are to be stored column-wise in V).
groups
A vector of nrow(V) integers labeling each group vertex in the partition.
mtype
The type of semi-projector used for the SCG. For now symmetric, laplacian and stochastic are available.
p
A probability vector of length nrow(V). p is the stationary probability distribution of a Markov chain when mtype = stochastic. This parameter is ignored otherwise.
norm
Either row or col. If set to row the rows of the Laplacian matrix sum to zero and the rows of the stochastic matrix sum to one; otherwise it is the columns.
Details

scgNormEps computes $\Vert v_i-Pv_i\Vert$, where $v_i$ is the $i$th eigenvector in V and $P$ is the projector corresponding to the mtype argument.

Value

  • normEps returns with a numeric vector whose $i$th component is $\Vert v_i-Pv_i\Vert$ (see Details).

References

D. Morton de Lachapelle, D. Gfeller, and P. De Los Rios, Shrinking Matrices while Preserving their Eigenpairs with Application to the Spectral Coarse Graining of Graphs. Submitted to SIAM Journal on Matrix Analysis and Applications, 2008. http://people.epfl.ch/david.morton

See Also

SCG and scg.

Aliases
  • scgNormEps
Examples
v <- rexp(20)
km <- kmeans(v,5)
sum(km$withinss)
scgNormEps(cbind(v), km$cluster)^2
Documentation reproduced from package igraph, version 0.6.5-2, License: GPL (>= 2)

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