Attack Robustness and Centrality of Complex Networks

By Swami Iyer, Timothy Killingback, Bala Sundaram, Zhen Wang. PLoS ONE, 2013

448 citations recorded by the source corpus.

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.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/82148c4561238a522a3b7d2298c1985480cbb25a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Experimental and Cognitive Psychology

In Statistical and Nonlinear Physics

Citations in the corpus

Vital nodes identification in complex networks (2016, methodology): “For example, in the study of network vulnerability, the degree-targeted attack can destroy the scale-free networks and exponential networks very effectively, comparing with some selecting methods based on more complicated centralities like betweenness centrality, closeness centrality and eigenvector”