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.

## 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()`

Spectral Coarse Graining
`scg-method`

,
`scg_eps()`

,
`scg_group()`

,
`scg()`

,
`stochastic_matrix()`

## Author

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

## 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$R)
#> 4 x 4 sparse Matrix of class "dsCMatrix"
#>
#> [1,] 1 . . .
#> [2,] . 1 . .
#> [3,] . . 1 .
#> [4,] . . . 1
P <- crossprod(lr$R) # same as t(lr$R) %*% lr$R
# P is an orthogonal projector
isSymmetric(P)
#> [1] TRUE
sum((P %*% P - P)^2)
#> [1] 3.5206e-31
## 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)
#> [1] 1.110223e-16 1.110223e-16 1.110223e-16 1.110223e-16
```