Robustness envelopes of networks

Robustness envelopes of networks

We study the robustness of networks under node removal, considering random node failure, as well as targeted node attacks based on network centrality measures. Whilst both of these have been studied in the literature, existing approaches tend to study random failure in terms of average-case behavior, giving no idea of how badly network performance can degrade purely by chance. Instead of consid...

Robustness of networks

Robustness of large networks

The increasing importance of large networks in our “linked” world necessitates to enhance our understanding of their most characteristic behaviors. We discuss how to describe large networks and argue that a stochastic approach is most appropriate. The “robustness” of large networks is not uniquely defined. Here, the robustness is approached from two angles: we are studying the influence of the ...

Emergence of robustness in networks of networks.

A model of interdependent networks of networks (NONs) was introduced recently [Proc. Natl. Acad. Sci. (USA) 114, 3849 (2017)PNASA60027-842410.1073/pnas.1620808114] in the context of brain activation to identify the neural collective influencers in the brain NON. Here we investigate the emergence of robustness in such a model, and we develop an approach to derive an exact expression for the rand...

Robustness in Consensus Networks

We consider the problem of robustness in large consensus networks that occur in many areas such as distributed optimization. Robustness, in this context, is the scaling of performance measures, e.g.: H2–norm, as a function of network dimension. We provide a formal framework to quantify the relation between such performance scaling and the convergence speed of the network. Specifically, we provi...