Using Machine Learning to Quantify the Robustness of Network Controllability

Conference paper (2021)

Authors

P. Sun Network Architectures and Services - EEMCS
Robert Kooij Network Architectures and Services - EEMCS
Research Group
Network Architectures and Services (EEMCS) (TU Delft)
Copyright: © 2021 Ashish Dhiman, P. Sun, Robert Kooij
DOI: https://doi.org/10.1007/978-3-030-70866-5_2
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Quantum & Computer Engineering

Research Group

Network Architectures and Services

Abstract

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 the existing closed-form analytical approximations in case of both synthetic and real-world networks. Apart from targeted attacks based upon the removal of so-called critical links, we also propose analytical approximations for out-in degree-based attacks.

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