R igraph manual pages

scg_semi_proj {igraph}

R Documentation

Semi-Projectors

Description

A function to compute the L and R semi-projectors for a given partition of the vertices.

Usage

scg_semi_proj(
  groups,
  mtype = c("symmetric", "laplacian", "stochastic"),
  p = NULL,
  norm = c("row", "col"),
  sparse = igraph_opt("sparsematrices")
)

Arguments

groups	A vector of nrow(X) or vcount(X) integers giving the group label of every vertex in the partition.
mtype	The type of semi-projectors. For now “symmetric”, “laplacian” and “stochastic” are available.
p	A probability vector of length length(gr). p is the stationary probability distribution of a Markov chain when mtype = “stochastic”. This parameter is ignored in all other cases.
norm	Either “row” or “col”. If set to “row” the rows of the Laplacian matrix sum up to zero and the rows of the stochastic sum up to one; otherwise it is the columns.
sparse	Logical scalar, whether to return sparse matrices.

Details

The three types of semi-projectors are defined as follows. Let \gamma(j) label the group of vertex j in a partition of all the vertices.

The symmetric semi-projectors are defined as

L_{\alpha j}=R_{\alpha j}=

\frac{1}{\sqrt{|\alpha|}}\delta_{\alpha\gamma(j)},

the (row) Laplacian semi-projectors as

L_{\alpha j}=\frac{1}{|\alpha|}\delta_{\alpha\gamma(j)}\,\,\,\,

\textrm{and}\,\,\,\, R_{\alpha j}=\delta_{\alpha\gamma(j)},

and the (row) stochastic semi-projectors as

L_{\alpha j}=\frac{p_{1}(j)}{\sum_{k\in\gamma(j)}p_{1}(k)}\,\,\,\,

\textrm{and}\,\,\,\, R_{\alpha j}=\delta_{\alpha\gamma(j)\delta_{\alpha\gamma(j)}},

where p_1 is the (left) eigenvector associated with the one-eigenvalue of the stochastic matrix. L and R are defined in a symmetric way when norm = col. All these semi-projectors verify various properties described in the reference.

Value

L	The semi-projector L.
R	The semi-projector R.

Author(s)

David Morton de Lachapelle, http://people.epfl.ch/david.morton.

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-method for a detailed introduction. scg, scg_eps, scg_group

Examples

library(Matrix)
# compute the semi-projectors and projector for the partition
# provided by a community detection method
g <- sample_pa(20, m = 1.5, directed = FALSE)
eb <- cluster_edge_betweenness(g)
memb <- membership(eb)
lr <- scg_semi_proj(memb)
#In the symmetric case L = R
tcrossprod(lr$R)  # same as lr$R %*% t(lr$R)
P <- crossprod(lr$R)  # same as t(lr$R) %*% lr$R
#P is an orthogonal projector
isSymmetric(P)
sum( (P %*% P-P)^2 )

## use L and R to coarse-grain the graph Laplacian
lr <- scg_semi_proj(memb, mtype="laplacian")
L <- laplacian_matrix(g)
Lt <- lr$L %*% L %*% t(lr$R)
## or better lr$L %*% tcrossprod(L,lr$R)
rowSums(Lt)

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