Using Machine Learning to Quantify the Robustness of Network Controllability

Ashish Dhiman, Peng Sun*, Robert Kooij

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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.

Original languageEnglish
Title of host publicationMachine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers
EditorsÉric Renault, Selma Boumerdassi, Paul Mühlethaler
PublisherSpringer
Pages19-39
Number of pages21
ISBN (Print)9783030708658
DOIs
Publication statusPublished - 2021
Event3rd International Conference on Machine Learning for Networking, MLN 2020 - Paris, France
Duration: 24 Nov 202026 Nov 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12629 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Machine Learning for Networking, MLN 2020
CountryFrance
CityParis
Period24/11/2026/11/20

Keywords

  • Driver nodes
  • Machine learning
  • Network controllability
  • Network robustness

Fingerprint

Dive into the research topics of 'Using Machine Learning to Quantify the Robustness of Network Controllability'. Together they form a unique fingerprint.

Cite this