Robustness of Network Controllability with Respect to Node Removals

Abstract

Network controllability and its robustness has been widely studied. However, analytical methods to calculate network controllability with respect to node removals are currently lacking. This paper develops methods, based upon generating functions for the in-and out-degree distributions, to approximate the minimum number of driver nodes needed to control directed networks, during random and targeted node removals. By validating the proposed methods on synthetic and real-world networks, we show that our methods work very well in the case of random node removals and reasonably well in the case of targeted node removals, in particular for moderate fractions of attacked nodes.

Robustness of Network Controllability with Respect to Node Removals Based on In-Degree and Out-Degree

“…Chen et al [ 18 ] developed analytical approximations for the minimum number of driver nodes during random link removal using generating functions. Wang et al [ 19 ] later conducted analytical methods based on generating functions to approximate the network controllability during random and targeted node removal based on the total degree of different kinds of networks. In addition to analytical methods, machine learning has been employed to predict network controllability robustness.…”