Using Machine Learning to Quantify the Robustness of Network Controllability

Ashish Dhiman; Peng Sun; Robert Kooij

doi:10.1007/978-3-030-70866-5_2

Using Machine Learning to Quantify the Robustness of Network Controllability

Ashish Dhiman, Peng Sun^*, Robert Kooij

^*Corresponding author for this work

Network Architectures and Services

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

10 Citations (Scopus)

44 Downloads (Pure)

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 language	English
Title of host publication	Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers
Editors	Éric Renault, Selma Boumerdassi, Paul Mühlethaler
Publisher	Springer
Pages	19-39
Number of pages	21
ISBN (Print)	9783030708658
DOIs	https://doi.org/10.1007/978-3-030-70866-5_2
Publication status	Published - 2021
Event	3rd International Conference on Machine Learning for Networking, MLN 2020 - Paris, France Duration: 24 Nov 2020 → 26 Nov 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12629 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Machine Learning for Networking, MLN 2020
Country/Territory	France
City	Paris
Period	24/11/20 → 26/11/20

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Driver nodes
Machine learning
Network controllability
Network robustness

Access to Document

10.1007/978-3-030-70866-5_2

Dhiman2021_Chapter_UsingMachineLearningToQuantifyFinal published version, 2.49 MB

Cite this

Dhiman, A., Sun, P., & Kooij, R. (2021). Using Machine Learning to Quantify the Robustness of Network Controllability. In É. Renault, S. Boumerdassi, & P. Mühlethaler (Eds.), Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers (pp. 19-39). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12629 LNCS). Springer. https://doi.org/10.1007/978-3-030-70866-5_2

Dhiman, Ashish ; Sun, Peng ; Kooij, Robert. / Using Machine Learning to Quantify the Robustness of Network Controllability. Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers. editor / Éric Renault ; Selma Boumerdassi ; Paul Mühlethaler. Springer, 2021. pp. 19-39 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Using Machine Learning to Quantify the Robustness of Network Controllability",

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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Dhiman, A, Sun, P & Kooij, R 2021, Using Machine Learning to Quantify the Robustness of Network Controllability. in É Renault, S Boumerdassi & P Mühlethaler (eds), Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12629 LNCS, Springer, pp. 19-39, 3rd International Conference on Machine Learning for Networking, MLN 2020, Paris, France, 24/11/20. https://doi.org/10.1007/978-3-030-70866-5_2

Using Machine Learning to Quantify the Robustness of Network Controllability. / Dhiman, Ashish; Sun, Peng; Kooij, Robert.
Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers. ed. / Éric Renault; Selma Boumerdassi; Paul Mühlethaler. Springer, 2021. p. 19-39 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12629 LNCS).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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PY - 2021

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AB - 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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Dhiman A, Sun P, Kooij R. Using Machine Learning to Quantify the Robustness of Network Controllability. In Renault É, Boumerdassi S, Mühlethaler P, editors, Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers. Springer. 2021. p. 19-39. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-70866-5_2

Using Machine Learning to Quantify the Robustness of Network Controllability

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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