vis_clustered_graphs: Visualize clustered graphs

Description

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,
  ...
)

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;

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.

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

Run this code

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))

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