Attack Robustness and Centrality of Complex Networks

TLDR

Complex networks model many systems, and their robustness to component failure depends on how network structure changes when vertices are removed, a topic previously studied for random, degree‑based, and betweenness‑based attacks. This study extends prior work by examining how removing vertices chosen by a broader set of non‑local importance measures affects network structure. The authors analyze the impact of such targeted removals on model networks with varying degree distributions, clustering, assortativity, and on several empirical networks.

Abstract

Many complex systems can be described by networks, in which the constituent components are represented by vertices and the connections between the components are represented by edges between the corresponding vertices. A fundamental issue concerning complex networked systems is the robustness of the overall system to the failure of its constituent parts. Since the degree to which a networked system continues to function, as its component parts are degraded, typically depends on the integrity of the underlying network, the question of system robustness can be addressed by analyzing how the network structure changes as vertices are removed. Previous work has considered how the structure of complex networks change as vertices are removed uniformly at random, in decreasing order of their degree, or in decreasing order of their betweenness centrality. Here we extend these studies by investigating the effect on network structure of targeting vertices for removal based on a wider range of non-local measures of potential importance than simply degree or betweenness. We consider the effect of such targeted vertex removal on model networks with different degree distributions, clustering coefficients and assortativity coefficients, and for a variety of empirical networks.