Measuring the robustness of network controllability

Abstract

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 point for data transmission, a redistribution point or in physical terms, an entity that is capable of receiving, transmitting or redistributing information and a link could be a wired or a wireless connection between the nodes. It is of prime importance that the networks perform their functions properly. To ensure the effective operation of such networks, we need to control them by applying external inputs on some nodes which are known as driver nodes. We say that a network is controllable if it can be driven from any arbitrary state to any desired state in finite time under the control of driver nodes which are attached to the external inputs. Networks are often confronted with perturbations in the form of targeted and random attacks to disrupt their operation. Such perturbations make these networks less controllable. Thus, more driver nodes are needed to gain the full controllability of these networks. As a result, the robustness of network controllability decreases. In this study, the minimum number of driver nodes which gain full controllability after failures or attacks is chosen as the robustness metric.
Existing closed-form analytical approximations estimate the normalized minimum number of driver nodes as a function of the fraction of removed links in both targeted and random attacks. However, the approximations sometimes do not fit with the simulations and the errors between the approximations and simulations are large. The main objectives of this study are to improve the analytical approximations using machine learning methods for both targeted and random attacks and additionally, derive a suitable analytical approximation for the out-in degree-based attack. Specifically, we use Linear Regression, Random Forest, and Artificial Neural Networks. To evaluate the performance of our machine learning models, we compare them with analytical approximations and simulations. In addition to this, we also study the attack based variability in estimating the minimum number of driver nodes using robustness envelopes.
Based on the performance evaluations, we found that the approximations using machine learning models significantly outperform the existing closed-form analytical approximations for the minimum number of driver nodes, both in real-world and synthetic networks. Furthermore, we also assess the performance of our analytical approximations for the out-in degree-based attacks by comparing them with simulations.