plot_robustness: Plot Network Robustness

In cograph: Analysis and Visualization of Complex Networks

Plot Network Robustness

Description

Creates a visualization of network robustness showing the fraction of remaining nodes in the largest connected component during sequential node/edge removal. Supports comparison of multiple attack strategies.

Usage

plot_robustness(
  ...,
  x = NULL,
  measures = c("betweenness", "degree", "random"),
  colors = NULL,
  title = "Network Robustness: sequential removal of nodes",
  xlab = "Fraction of removed nodes",
  ylab = "Fraction of remaining nodes",
  lwd = 1.5,
  legend_pos = "topright",
  n_iter = 1000,
  seed = NULL,
  type = "vertex"
)

Arguments

...	One or more robustness results from robustness, or named arguments to pass networks for on-the-fly computation.
x	Network for computing robustness on-the-fly.
measures	Character vector of attack strategies to compare. Default c("betweenness", "degree", "random").
colors	Named vector of colors. Default: green=Degree, red=Betweenness, blue=Random (matching Nature paper style).
title	Plot title. Default "Network Robustness: sequential removal of nodes".
xlab	X-axis label. Default "Fraction of removed nodes".
ylab	Y-axis label. Default "Fraction of remaining nodes".
lwd	Line width. Default 1.5.
legend_pos	Legend position. Default "topright".
n_iter	Number of iterations for random. Default 1000.
seed	Random seed. Default NULL.
type	Removal type. Default "vertex".

Value

Invisibly returns combined data frame of all robustness results.

Examples

if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::sample_pa(50, m = 2, directed = FALSE)

  # Quick comparison of all strategies
  plot_robustness(x = g, n_iter = 20)

  # Or compute separately
  rob1 <- robustness(g, measure = "betweenness")
  rob2 <- robustness(g, measure = "degree")
  rob3 <- robustness(g, measure = "random", n_iter = 20)
  plot_robustness(rob1, rob2, rob3)
}

cograph documentation built on April 1, 2026, 1:07 a.m.