An S4 Class implementing the Kamada Kawai Algorithm for graph embedding.

## Details

Graph embedding algorithms se the data as a graph. Between the nodes of the graph exist attracting and repelling forces which can be modeled as electrical fields or springs connecting the nodes. The graph is then forced into a lower dimensional representation that tries to represent the forces betweent he nodes accurately by minimizing the total energy of the attracting and repelling forces.

## Slots

`fun`

A function that does the embedding and returns a dimRedResult object.

`stdpars`

The standard parameters for the function.

## General usage

Dimensionality reduction methods are S4 Classes that either be used
directly, in which case they have to be initialized and a full
list with parameters has to be handed to the `@fun()`

slot, or the method name be passed to the embed function and
parameters can be given to the `...`

, in which case
missing parameters will be replaced by the ones in the
`@stdpars`

.

## Parameters

KamadaKawai can take the following parameters:

- ndim
The number of dimensions, defaults to 2. Can only be 2 or 3

- knn
Reduce the graph to keep only the neares neighbors. Defaults to 100.

- d
The distance function to determine the weights of the graph edges. Defaults to euclidean distances.

## Implementation

Wraps around `layout_with_kk`

. The parameters
maxiter, epsilon and kkconst are set to the default values and
cannot be set, this may change in a future release. The DimRed
Package adds an extra sparsity parameter by constructing a knn
graph which also may improve visualization quality.

## References

Kamada, T., Kawai, S., 1989. An algorithm for drawing general undirected graphs. Information Processing Letters 31, 7-15. https://doi.org/10.1016/0020-0190(89)90102-6

## See also

Other dimensionality reduction methods:
`AutoEncoder-class`

,
`DRR-class`

,
`DiffusionMaps-class`

,
`DrL-class`

,
`FastICA-class`

,
`FruchtermanReingold-class`

,
`HLLE-class`

,
`Isomap-class`

,
`MDS-class`

,
`NNMF-class`

,
`PCA-class`

,
`PCA_L1-class`

,
`UMAP-class`

,
`dimRedMethod-class`

,
`dimRedMethodList()`

,
`kPCA-class`

,
`nMDS-class`

,
`tSNE-class`

## Examples

```
if(requireNamespace(c("igraph", "coRanking"), quietly = TRUE)) {
dat <- loadDataSet("Swiss Roll", n = 200)
emb <- embed(dat, "KamadaKawai")
plot(emb, type = "2vars")
}
```