Reachability-based Robustness of Controllability in Sparse Communication Networks

In this paper, we propose closed-form analytic approximations for the number of controllable nodes in sparse communication networks from the aspect of network controllability, considering link-based random attack, targeted attack, as well as random attack under the protection of critical links. We compare our approximations with simulation...

Using Machine Learning to Quantify the Robustness of Network Controllability

This paper presents machine learning based approximations for the minimum number of driver nodes needed for structural controllability of networks under link-based random and targeted attacks. We compare our approximations with existing analytical approximations and show that our machine learning based approximations significantly outperform...

Measuring the robustness of network controllability

Networks are all around us, telecommunication networks, road transportation networks, and the Internet are a few examples of networks that we encounter every day. The entities in a network are represented by nodes and the interconnections between them are represented by links. For example, in a telecommunication network, a node could be an end...