research-article

Robustness Estimation of Infrastructure Networks: On the Usage of Degree Centrality

Authors:

Sebastian Wandelt,

Xiaoqian SunAuthors Info & Claims

ARES '18: Proceedings of the 13th International Conference on Availability, Reliability and Security

Article No.: 45, Pages 1 - 7

https://doi.org/10.1145/3230833.3232822

Published: 27 August 2018 Publication History

Abstract

This study aims to clarify a few concepts when assessing the robustness of complex systems with the usage of complex network methods. Many researchers simply use the degree centrality as a proxy for node importance in a network. We show that such assumption can lead to rather overestimated robustness values, neglecting much stronger attacks.

Furthermore, we highlight the state-of-the-art in the area of network dismantling, a sub-area of complex networks, emerged in the last 2-3 years. Three case studies on real infrastructure networks, including subway networks, air transportation networks, and power grid networks, are used here to show the consequences of using an inappropriate proxy for robustness. Moreover, we highlight the trade-off between solution quality and computational cost for larger networks. We hope that our study helps to choose an appropriate, recent method from network dismantling instead of simple using degree centrality, when analyzing the robustness of complex networks.

References

[1]

Réka Albert and Albert-László Barabási. 2002. Statistical mechanics of complex networks. Reviews of modern physics 74, 1 (2002), 47.

[2]

Réka Albert, Hawoong Jeong, and Albert-László Barabási. 2000. Error and attack tolerance of complex networks. Nature 406 (2000), 378--382.

[3]

J Ash and D Newth. 2007. Optimizing complex networks for resilience against cascading failure. Physica A: Statistical Mechanics and its Applications 380 (2007), 673--683.

[4]

Michael Boss, Helmut Elsinger, Martin Summer, and Stefan Thurner. 2004. Network topology of the interbank market. Quantitative Finance 4, 6 (2004), 677--684.

[5]

Alfredo Braunstein, Luca Dall.Asta, Guilhem Semerjian, and Lenka Zdeborová. 2016. Network dismantling. Proceedings of the National Academy of Sciences (2016), 201605083.

[6]

Duncan S Callaway, Mark EJ Newman, Steven H Strogatz, and Duncan J Watts. 2000. Network robustness and fragility: Percolation on random graphs. Physical review le.ers 85, 25 (2000), 5468.

[7]

Reuven Cohen, Keren Erez, Daniel Ben-Avraham, and Shlomo Havlin. 2000. Resilience of the Internet to random breakdowns. Physical review le.ers 85, 21 (2000), 4626.

[8]

Reuven Cohen and Shlomo Havlin. 2010. Complex networks: structure, robustness and function. Cambridge University Press.

[9]

Lucas Cuadra, Sancho Salcedo-Sanz, Javier Del Ser, Silvia Jiménez-Fernández, and Zong Woo Geem. 2015. A critical review of robustness in power grids using complex networks concepts. Energies 8, 9 (2015), 9211--9265.

[10]

Linton C. Freeman. 1977. A set of measures of centrality based on betweenness. Sociometry 40 (1977), 35--41.

[11]

Jianxi Gao, Xueming Liu, Daqing Li, and Shlomo Havlin. 2015. Recent Progress on the Resilience of Complex Networks. Energies 8, 10 (2015), 12187.

[12]

Petter Holme, Beom Jun Kim, Chang No Yoon, and Seung Kee Han. 2002. Attack vulnerability of complex networks. Physical Review E 65 (2002).

[13]

Xuqing Huang, Jianxi Gao, Sergey V Buldyrev, Shlomo Havlin, and H Eugene Stanley. 2011. Robustness of interdependent networks under targeted attack. Physical Review E 83, 6 (2011), 065101.

[14]

Yusoon Kim, Yi-Su Chen, and Kevin Linderman. 2015. Supply network disruption and resilience: A network structural perspective. Journal of Operations Management 33 (2015), 43--59.

Digital Library

[15]

Maksim Kitsak, Lazaros K. Gallos, Shlomo Havlin, Fredrik Liljeros, Lev Muchnik, H. Eugene Stanley, and Hernan A. Makse. 2010. Identi.cation of in.uential spreaders in complex networks. Nature Physics 6 (2010), 888--893.

[16]

F Morone and HA Makse. 2015. Influence maximization in complex networks through optimal percolation. Nature 524 (2015).

[17]

Mark EJ Newman. 2010. Networks-An Introduction. Oxford University Press.

[18]

M. E. J. Newman. 2013. Community detection and graph partitioning. EPL (Europhysics Letters) 103, 2 (2013), 28003. http://stacks.iop.org/0295-5075/103/i= 2/a=28003

[19]

Xiao-Long Ren, Niels Gleinig, Dirk Helbing, and Nino Antulov-Fantulin. 2018. Generalized Network Dismantling. CoRR abs/1801.01357 (2018). arXiv:1801.01357 http://arxiv.org/abs/1801.01357

[20]

Christian M Schneider, André A Moreira, José S Andrade, Shlomo Havlin, and Hans J Herrmann. 2011. Mitigation of malicious attacks on networks. Proceedings of the National Academy of Sciences 108, 10 (2011), 3838--3841.

[21]

Xiaoqian Sun and SebastianWandelt. 2014. Network similarity analysis of air navigation route systems. Transportation Research Part E: Logistics and Transportation Review 70, 0 (2014), 416--434.

[22]

Xiaoqian Sun, Sebastian Wandelt, and Florian Linke. 2015. Temporal evolution analysis of the European air transportation system: air navigation route network and airport network. Transportmetrica B: Transport Dynamics 3, 2 (2015), 153--168.

[23]

Zhixiao Wang, Ya Zhao, Jingke Xi, and Changjiang Du. 2016. Fast ranking influential nodes in complex networks using a k-shell iteration factor. Physica A: Statistical Mechanics and its Applications 461 (2016), 171--181.

[24]

Yang Yang, Takashi Nishikawa, and Adilson E. Motter. 2017. Small vulnerable sets determine large network cascades in power grids. Science 358, 6365 (2017). arXiv:http://science.sciencemag.org/content/358/6365/eaan3184.full.pdf

[25]

Soon-Hyung Yook, Hawoong Jeong, and Albert-László Barabási. 2002. Modeling the Internet's large-scale topology. Proceedings of the National Academy of Sciences 99, 21 (2002), 13382--13386.

[26]

Ramin Zahedi and Mohammad Khansari. 2015. A new immunization algorithm based on spectral properties for complex networks. Journal of Statistical Mechanics Theory and Experiment (October 2015).

[27]

Massimiliano Zanin and Fabrizio Lillo. 2013. Modelling the air transport with complex networks: A short review. The European Physical Journal Special Topics 215, 1 (2013), 5--21.

[28]

Lenka Zdeborová, Pan Zhang, and Hai-Jun Zhou. 2016. Fast and simple decycling and dismantling of networks. Scientific reports 6 (2016).

Cited By

Goswami DDzaferagic MSiljak HSekhar Das SMarchetti N(2023)Analysis of Temporal Robustness in Massive Machine Type CommunicationsIEEE Internet of Things Journal10.1109/JIOT.2022.322874510:8(6915-6927)Online publication date: 15-Apr-2023
https://doi.org/10.1109/JIOT.2022.3228745
Rajeh SCherifi H(2022)Ranking influential nodes in complex networks with community structurePLOS ONE10.1371/journal.pone.027361017:8(e0273610)Online publication date: 29-Aug-2022
https://doi.org/10.1371/journal.pone.0273610
Sun XWandelt S(2021)Robustness of Air Transportation as Complex Networks:Systematic Review of 15 Years of Research and Outlook into the FutureSustainability10.3390/su1311644613:11(6446)Online publication date: 6-Jun-2021
https://doi.org/10.3390/su13116446
Show More Cited By

Index Terms

Robustness Estimation of Infrastructure Networks: On the Usage of Degree Centrality
1. Networks
  1. Network algorithms
  2. Network properties
    1. Network reliability

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cover image ACM Other conferences

ARES '18: Proceedings of the 13th International Conference on Availability, Reliability and Security

August 2018

603 pages

ISBN:9781450364485

DOI:10.1145/3230833

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Universität Hamburg: Universität Hamburg

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Publication History

Published: 27 August 2018

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ARES 2018

ARES 2018: International Conference on Availability, Reliability and Security

August 27 - 30, 2018

Hamburg, Germany

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ARES '18 Paper Acceptance Rate 128 of 260 submissions, 49%;

Overall Acceptance Rate 228 of 451 submissions, 51%

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Cited By

Goswami DDzaferagic MSiljak HSekhar Das SMarchetti N(2023)Analysis of Temporal Robustness in Massive Machine Type CommunicationsIEEE Internet of Things Journal10.1109/JIOT.2022.322874510:8(6915-6927)Online publication date: 15-Apr-2023
https://doi.org/10.1109/JIOT.2022.3228745
Rajeh SCherifi H(2022)Ranking influential nodes in complex networks with community structurePLOS ONE10.1371/journal.pone.027361017:8(e0273610)Online publication date: 29-Aug-2022
https://doi.org/10.1371/journal.pone.0273610
Sun XWandelt S(2021)Robustness of Air Transportation as Complex Networks:Systematic Review of 15 Years of Research and Outlook into the FutureSustainability10.3390/su1311644613:11(6446)Online publication date: 6-Jun-2021
https://doi.org/10.3390/su13116446
Sano HBerton L(2021)A Temporal Event Graph Approach and Robustness Analysis for Air Transport NetworkIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.31145358:4(3453-3464)Online publication date: 1-Oct-2021
https://doi.org/10.1109/TNSE.2021.3114535
Rajeh SSavonnet MLeclercq ECherifi H(2021)Characterizing the interactions between classical and community-aware centrality measures in complex networksScientific Reports10.1038/s41598-021-89549-x11:1Online publication date: 12-May-2021
https://doi.org/10.1038/s41598-021-89549-x
Sano HBerton LHung CCerny TShin DBechini A(2020)Topology and robustness analysis of temporal air transport networkProceedings of the 35th Annual ACM Symposium on Applied Computing10.1145/3341105.3374062(1881-1884)Online publication date: 30-Mar-2020
https://dl.acm.org/doi/10.1145/3341105.3374062

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