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 containg 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.

See also

vis_clustered_graphs for visualizing clustered graphs

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))