cograph (version 2.0.0)

plot_robustness: 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"
)

Value

Invisibly returns combined data frame of all robustness results.

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".

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)
}