robustness {brainGraph}

Analysis of network robustness

Description

This function performs a “targeted attack” of a graph or a “random failure” analysis, calculating the size of the largest component after edge or vertex removal.

Usage

robustness(g, type = c("vertex", "edge"), measure = c("btwn.cent",
  "degree", "random"), N = 1000)

Arguments

g

An igraph graph object

type

Character string; either 'vertex' or 'edge' removals. Default: vertex

measure

Character string; sort by either 'btwn.cent' or 'degree', or choose 'random'. Default: 'btwn.cent'

N

Integer; the number of iterations if 'random' is chosen. Default: 1e3

Details

In a targeted attack, it will sort the vertices by either degree or betweenness centrality (or sort edges by betweenness), and successively remove the top vertices/edges. Then it calculates the size of the largest component.

In a random failure analysis, vertices/edges are removed in a random order.

Value

Data table with elements:

type

Character string describing the type of analysis performed

measure

The input argument measure

comp.size

The size of the largest component after edge/vertex removal

comp.pct

Numeric vector of the ratio of maximal component size after each removal to the observed graph's maximal component size

removed.pct

Numeric vector of the ratio of vertices/edges removed

Group

Character string indicating the subject group, if applicable

Author(s)

Christopher G. Watson, [e-mail]

References

Albert, R. and Jeong, H. and Barabasi, A. (2000) Error and attack tolerance of complex networks. Nature, 406, 378–381. doi: 10.1038/35019019