The idea of clustered graphs is to reduce the complexity of an ego-centered network
graph by visualizing alters in clusters defined by a categorical variable (Lerner et al. 2008).
`clustered_graphs()`

calculates group sizes, inter and intra group tie
densities and returns these informations in a `list`

of `igraph`

objects.

```
clustered_graphs(object, ..., clust.groups)
# S3 method for list
clustered_graphs(object, aaties, clust.groups, ...)
# S3 method for egor
clustered_graphs(object, clust.groups, ...)
# S3 method for data.frame
clustered_graphs(object, aaties, clust.groups, egoID = ".egoID", ...)
```

## Arguments

- object
An `egor`

object.

- ...
arguments to be passed to methods

- clust.groups
A `character`

naming the `factor`

variable defining the groups.

- aaties
`data.frame/ list`

containing alter-alter relations as a
'global edge list' or as a list of 'edge lists'.
(not needed if `object`

is an `egor`

object).

- egoID
`Character`

. Name of the variable identifying egos (default: "egoID").

## Value

`clustered_graphs`

returns a list of graph objects representing
the clustered ego-centered network data;

## References

Brandes, U., Lerner, J., Lubbers, M. J., McCarty, C., & Molina,
J. L. (2008). Visual Statistics for Collections of Clustered Graphs. 2008
IEEE Pacific Visualization Symposium, 47-54.

## Examples

```
data("egor32")
# Simplify networks to clustered graphs, stored as igraph objects
graphs <- clustered_graphs(egor32, "country")
# Visualise
par(mfrow = c(2,3))
vis_clustered_graphs(
graphs[1:5]
)
par(mfrow = c(1,1))
```