Visualize clustered graphs

vis_clustered_graphs visualizes clustered_graphs using a list of clustered graphs created with clustered_graphs.

Usage

vis_clustered_graphs(
  graphs,
  node.size.multiplier = 1,
  node.min.size = 0,
  node.max.size = 200,
  normalise.node.sizes = TRUE,
  edge.width.multiplier = 1,
  center = 1,
  label.size = 0.8,
  labels = FALSE,
  legend.node.size = 45,
  pdf.name = NULL,
  ...
)

Arguments

graphs

List of graph objects, representing the clustered graphs.

node.size.multiplier

Numeric used to multiply the node diameter of visualized nodes.

node.min.size

Numeric indicating minimum size of plotted nodes

node.max.size

Numeric indicating maximum size of plotted nodes

normalise.node.sizes

Logical. If TRUE (default) node sizes are plotted using per network proportions rather than counts.

edge.width.multiplier

Numeric used to multiply the edge width.

center

Numeric indicating the vertex to be plotted in center.

label.size

Numeric.

labels

Boolean. Plots with turned off labels will be accompanied by a 'legend' plot giving the labels of the vertices.

legend.node.size

Numeric used as node diameter of legend graph.

pdf.name

Character giving the name/path of the pdf file to create.

...

Arguments to pass to plot.igraph.

Value

vis_clustered_graphs plots a list of igraph objects created by the clustered_graphs

function.

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

clustered_graphs for creating clustered graphs objects

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))