Use this if you are using igraph from R
This function handles all the steps involved in the Spectral Coarse Graining (SCG) of some matrices and graphs as described in the reference below.
scg( X, ev, nt, groups = NULL, mtype = c("symmetric", "laplacian", "stochastic"), algo = c("optimum", "interv_km", "interv", "exact_scg"), norm = c("row", "col"), direction = c("default", "left", "right"), evec = NULL, p = NULL, use.arpack = FALSE, maxiter = 300, sparse = igraph_opt("sparsematrices"), output = c("default", "matrix", "graph"), semproj = FALSE, epairs = FALSE, stat.prob = FALSE )
X 
The input graph or square matrix. Can be of class 
ev 
A vector of positive integers giving the indexes of the eigenpairs to be preserved. For real eigenpairs, 1 designates the eigenvalue with largest algebraic value, 2 the one with second largest algebraic value, etc. In the complex case, it is the magnitude that matters. 
nt 
A vector of positive integers of length one or equal to

groups 
A vector of 
mtype 
Character scalar. The type of semiprojector to be used for the SCG. For now “symmetric”, “laplacian” and “stochastic” are available. 
algo 
Character scalar. The algorithm used to solve the SCG problem. Possible values are “optimum”, “interv\_km”, “interv” and “exact\_scg”. 
norm 
Character scalar. Either “row” or “col”. If set to “row” the rows of the Laplacian matrix sum up to zero and the rows of the stochastic matrix sum up to one; otherwise it is the columns. 
direction 
Character scalar. When set to “right”, resp.
“left”, the parameters 
evec 
A numeric matrix of (eigen)vectors to be preserved by the coarse
graining (the vectors are to be stored columnwise in 
p 
A probability vector of length 
use.arpack 
Logical scalar. When set to 
maxiter 
A positive integer giving the maximum number of iterations
for the kmeans algorithm when 
sparse 
Logical scalar. Whether to return sparse matrices in the result, if matrices are requested. 
output 
Character scalar. Set this parameter to “default” to
retrieve a coarsegrained object of the same class as 
semproj 
Logical scalar. Set this parameter to 
epairs 
Logical scalar. Set this to 
stat.prob 
Logical scalar. This is to collect the stationary
probability 
Please see scgmethod for an introduction.
In the following V is the matrix of eigenvectors for which the SCG is
solved. V is calculated from X
, if it is not given in the
evec
argument.
The algorithm “optimum” solves exactly the SCG problem for each
eigenvector in V
. The running time of this algorithm is O(max(nt) m^2) for the symmetric and laplacian matrix
problems (i.e. when mtype
is “symmetric” or
“laplacian”. It is O(m^3) for the stochastic problem. Here
m is the number of rows in V
. In all three cases, the memory
usage is O(m^2).
The algorithms “interv” and “interv\_km” solve approximately
the SCG problem by performing a (for now) constant binning of the components
of the eigenvectors, that is nt[i]
constantsize bins are used to
partition V[,i]
. When algo
= “interv\_km”, the (Lloyd)
kmeans algorithm is run on each partition obtained by “interv” to
improve accuracy.
Once a minimizing partition (either exact or approximate) has been found for
each eigenvector, the final grouping is worked out as follows: two vertices
are grouped together in the final partition if they are grouped together in
each minimizing partition. In general the size of the final partition is not
known in advance when ncol(V)
>1.
Finally, the algorithm “exact\_scg” groups the vertices with equal components in each eigenvector. The last three algorithms essentially have linear running time and memory load.
Xt 
The coarsegrained graph, or matrix, possibly a sparse matrix. 
groups 
A vector of 
L 
The semiprojector L if 
R 
The
semiprojector R if 
values 
The computed
eigenvalues if 
vectors 
The computed or
supplied eigenvectors if 
p 
The stationary
probability vector if 
David Morton de Lachapelle, http://people.epfl.ch/david.morton.
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
scgmethod for an introduction. scg_eps
,
scg_group
and scg_semi_proj
.
## We are not running these examples any more, because they ## take a long time (~20 seconds) to run and this is against the CRAN ## repository policy. Copy and paste them by hand to your R prompt if ## you want to run them. ## Not run: # SCG of a toy network g < make_full_graph(5) %du% make_full_graph(5) %du% make_full_graph(5) g < add_edges(g, c(1,6, 1,11, 6, 11)) cg < scg(g, 1, 3, algo="exact_scg") #plot the result layout < layout_with_kk(g) nt < vcount(cg$Xt) col < rainbow(nt) vsize < table(cg$groups) ewidth < round(E(cg$Xt)$weight,2) op < par(mfrow=c(1,2)) plot(g, vertex.color = col[cg$groups], vertex.size = 20, vertex.label = NA, layout = layout) plot(cg$Xt, edge.width = ewidth, edge.label = ewidth, vertex.color = col, vertex.size = 20*vsize/max(vsize), vertex.label=NA, layout = layout_with_kk) par(op) ## SCG of realworld network library(igraphdata) data(immuno) summary(immuno) n < vcount(immuno) interv < c(100,100,50,25,12,6,3,2,2) cg < scg(immuno, ev= n(1:9), nt=interv, mtype="laplacian", algo="interv", epairs=TRUE) ## are the eigenvalues wellpreserved? gt < cg$Xt nt < vcount(gt) Lt < laplacian_matrix(gt) evalt < eigen(Lt, only.values=TRUE)$values[nt(1:9)] res < cbind(interv, cg$values, evalt) res < round(res,5) colnames(res) < c("interv","lambda_i","lambda_tilde_i") rownames(res) < c("N1","N2","N3","N4","N5","N6","N7","N8","N9") print(res) ## use SCG to get the communities com < scg(laplacian_matrix(immuno), ev=nc(1,2), nt=2)$groups col < rainbow(max(com)) layout < layout_nicely(immuno) plot(immuno, layout=layout, vertex.size=3, vertex.color=col[com], vertex.label=NA) ## display the coarsegrained graph gt < simplify(as.undirected(gt)) layout.cg < layout_with_kk(gt) com.cg < scg(laplacian_matrix(gt), ntc(1,2), 2)$groups vsize < sqrt(as.vector(table(cg$groups))) op < par(mfrow=c(1,2)) plot(immuno, layout=layout, vertex.size=3, vertex.color=col[com], vertex.label=NA) plot(gt, layout=layout.cg, vertex.size=15*vsize/max(vsize), vertex.color=col[com.cg],vertex.label=NA) par(op) ## End(Not run)