IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v389y2010i23p5538-5549.html
   My bibliography  Save this article

Vulnerability of labeled networks

Author

Listed:
  • Trpevski, Daniel
  • Smilkov, Daniel
  • Mishkovski, Igor
  • Kocarev, Ljupco

Abstract

We propose a metric for vulnerability of labeled graphs that has the following two properties: (1) when the labeled graph is considered as an unlabeled one, the metric reduces to the corresponding metric for an unlabeled graph; and (2) the metric has the same value for differently labeled fully connected graphs, reflecting the notion that any arbitrarily labeled fully connected topology is equally vulnerable as any other. A vulnerability analysis of two real-world networks, the power grid of the European Union, and an autonomous system network, has been performed. The networks have been treated as graphs with node labels. The analysis consists of calculating characteristic path lengths between labels of nodes and determining largest connected cluster size under two node and edge attack strategies. Results obtained are more informative of the networks’ vulnerability compared to the case when the networks are modeled with unlabeled graphs.

Suggested Citation

  • Trpevski, Daniel & Smilkov, Daniel & Mishkovski, Igor & Kocarev, Ljupco, 2010. "Vulnerability of labeled networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(23), pages 5538-5549.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:23:p:5538-5549
    DOI: 10.1016/j.physa.2010.08.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437110006904
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2010.08.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Crucitti, Paolo & Latora, Vito & Marchiori, Massimo & Rapisarda, Andrea, 2003. "Efficiency of scale-free networks: error and attack tolerance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 320(C), pages 622-642.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Milena Oehlers & Benjamin Fabian, 2021. "Graph Metrics for Network Robustness—A Survey," Mathematics, MDPI, vol. 9(8), pages 1-48, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrea Avena-Koenigsberger & Xiaoran Yan & Artemy Kolchinsky & Martijn P van den Heuvel & Patric Hagmann & Olaf Sporns, 2019. "A spectrum of routing strategies for brain networks," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-24, March.
    2. Zhou, Yaoming & Wang, Junwei, 2018. "Efficiency of complex networks under failures and attacks: A percolation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 658-664.
    3. Viljoen, Nadia M. & Joubert, Johan W., 2016. "The vulnerability of the global container shipping network to targeted link disruption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 396-409.
    4. Zio, Enrico, 2016. "Challenges in the vulnerability and risk analysis of critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 137-150.
    5. César Yajure & Darihelen Montilla & Jose Emmanuel Ramirez-Marquez & Claudio M Rocco S, 2013. "Network vulnerability assessment via bi-objective optimization with a fragmentation approach as proxy," Journal of Risk and Reliability, , vol. 227(6), pages 576-585, December.
    6. Yingying Xing & Jian Lu & Shengdi Chen & Sunanda Dissanayake, 2017. "Vulnerability analysis of urban rail transit based on complex network theory: a case study of Shanghai Metro," Public Transport, Springer, vol. 9(3), pages 501-525, October.
    7. Kashyap, G. & Ambika, G., 2019. "Link deletion in directed complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 631-643.
    8. Kashin Sugishita & Yasuo Asakura, 2021. "Vulnerability studies in the fields of transportation and complex networks: a citation network analysis," Public Transport, Springer, vol. 13(1), pages 1-34, March.
    9. Jin Qin & Yuxin He & Linglin Ni, 2014. "Quantitative Efficiency Evaluation Method for Transportation Networks," Sustainability, MDPI, vol. 6(12), pages 1-15, November.
    10. Leonidas Siozos-Rousoulis & Dimitri Robert & Wouter Verbeke, 2021. "A study of the U.S. domestic air transportation network: temporal evolution of network topology and robustness from 2001 to 2016," Journal of Transportation Security, Springer, vol. 14(1), pages 55-78, June.
    11. Milena Oehlers & Benjamin Fabian, 2021. "Graph Metrics for Network Robustness—A Survey," Mathematics, MDPI, vol. 9(8), pages 1-48, April.
    12. Shen, Dongqin & Cao, Shanshan, 2018. "An efficient immunization strategy based on transmission limit in weighted complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 1-7.
    13. Stefano Martinazzi & Andrea Flori, 2020. "The evolving topology of the Lightning Network: Centralization, efficiency, robustness, synchronization, and anonymity," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.
    14. Iovanella, Antonio, 2024. "Exploiting network science in business process management: A conceptual framework," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    15. Xia Cao & Chuanyun Li & Wei Chen & Jinqiu Li & Chaoran Lin, 2020. "Research on the invulnerability and optimization of the technical cooperation innovation network based on the patent perspective—A case study of new energy vehicles," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-19, September.
    16. Sohn, Insoo, 2019. "A robust complex network generation method based on neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 593-601.
    17. Guo, Shengmin & Wu, Ruoqian & Tong, Qingfeng & Zeng, Guanwen & Yang, Jian & Chen, Long & Zhu, Tongyu & Lv, Weifeng & Li, Daqing, 2018. "Is city traffic damaged by torrential rain?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1073-1080.
    18. Rui Ding & Norsidah Ujang & Hussain Bin Hamid & Mohd Shahrudin Abd Manan & Rong Li & Safwan Subhi Mousa Albadareen & Ashkan Nochian & Jianjun Wu, 2019. "Application of Complex Networks Theory in Urban Traffic Network Researches," Networks and Spatial Economics, Springer, vol. 19(4), pages 1281-1317, December.
    19. Galvan, Giulio & Agarwal, Jitendra, 2020. "Assessing the vulnerability of infrastructure networks based on distribution measures," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    20. Xu, Xiaohan & Huang, Ailing & Shalaby, Amer & Feng, Qian & Chen, Mingyang & Qi, Geqi, 2024. "Exploring cascading failure processes of interdependent multi-modal public transit networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:389:y:2010:i:23:p:5538-5549. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.