IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v437y2015icp36-54.html
   My bibliography  Save this article

Detecting link failures in complex network processes using remote monitoring

Author

Listed:
  • Dhal, R.
  • Abad Torres, J.
  • Roy, S.

Abstract

We study whether local structural changes in a complex network can be distinguished from passive remote time-course measurements of the network’s dynamics. Specifically the detection of link failures in a network synchronization process from noisy measurements at a single network component is considered. By phrasing the detection task as a Maximum A Posteriori Probability hypothesis testing problem, we are able to obtain conditions under which the detection is (1) improved over the a priori and (2) asymptotically perfect, in terms of the network spectrum and graph. We find that, in the case where the detector has knowledge of the network’s state, perfect detection is possible under general connectivity conditions regardless of the measurement location. When the detector does not have state knowledge, a remote signature permits improved but not perfect detection, under the same connectivity conditions. At its essence, detectability is achieved because of the close connection between a network’s topology, its eigenvalues and local response characteristics.

Suggested Citation

  • Dhal, R. & Abad Torres, J. & Roy, S., 2015. "Detecting link failures in complex network processes using remote monitoring," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 36-54.
    • Handle: RePEc:eee:phsmap:v:437:y:2015:i:c:p:36-54
    DOI: 10.1016/j.physa.2015.04.024
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437115003921
    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.2015.04.024?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. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 2000. "Scale-free characteristics of random networks: the topology of the world-wide web," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 281(1), pages 69-77.
    2. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    3. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    4. Maslov, Sergei & Sneppen, Kim & Zaliznyak, Alexei, 2004. "Detection of topological patterns in complex networks: correlation profile of the internet," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 529-540.
    Full references (including those not matched with items on IDEAS)

    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. Salcedo-Sanz, S. & Cuadra, L., 2019. "Quasi scale-free geographically embedded networks over DLA-generated aggregates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1286-1305.
    2. Ruiz Vargas, E. & Mitchell, D.G.V. & Greening, S.G. & Wahl, L.M., 2014. "Topology of whole-brain functional MRI networks: Improving the truncated scale-free model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 151-158.
    3. Wei, Daijun & Deng, Xinyang & Zhang, Xiaoge & Deng, Yong & Mahadevan, Sankaran, 2013. "Identifying influential nodes in weighted networks based on evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2564-2575.
    4. Yin, Liang & Shi, Li-Chen & Zhao, Jun-Yan & Du, Song-Yang & Xie, Wen-Bo & Yuan, Fei & Chen, Duan-Bing, 2018. "Heterogeneous information network model for equipment-standard system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 935-943.
    5. Wang, Huan & Xu, Chuan-Yun & Hu, Jing-Bo & Cao, Ke-Fei, 2014. "A complex network analysis of hypertension-related genes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 166-176.
    6. Li, Jianyu & Zhou, Jie, 2007. "Chinese character structure analysis based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 629-638.
    7. Wang, Yanhui & Bi, Lifeng & Lin, Shuai & Li, Man & Shi, Hao, 2017. "A complex network-based importance measure for mechatronics systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 180-198.
    8. Curado, Manuel & Rodriguez, Rocio & Tortosa, Leandro & Vicent, Jose F., 2022. "Anew centrality measure in dense networks based on two-way random walk betweenness," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    9. Peter Roopnarine, 2013. "Ecology and the Tragedy of the Commons," Sustainability, MDPI, vol. 5(2), pages 1-25, February.
    10. Ying Wang & Xiangmei Li & Jiangfeng Li & Zhengdong Huang & Renbin Xiao, 2018. "Impact of Rapid Urbanization on Vulnerability of Land System from Complex Networks View: A Methodological Approach," Complexity, Hindawi, vol. 2018, pages 1-18, May.
    11. Gao, Cai & Wei, Daijun & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2013. "A modified evidential methodology of identifying influential nodes in weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5490-5500.
    12. Fetta, Angelico & Harper, Paul & Knight, Vincent & Williams, Janet, 2018. "Predicting adolescent social networks to stop smoking in secondary schools," European Journal of Operational Research, Elsevier, vol. 265(1), pages 263-276.
    13. Zhou, Bin & He, Zhe & Wang, Nianxin & Wang, Bing-Hong, 2016. "A method of characterizing network topology based on the breadth-first search tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 682-686.
    14. Chen, Qinghua & Shi, Dinghua, 2006. "Markov chains theory for scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(1), pages 121-133.
    15. Ding, Jin & Lu, Yong-Zai & Chu, Jian, 2013. "Studies on controllability of directed networks with extremal optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6603-6615.
    16. Li, Jianyu & Zhou, Jie & Luo, Xiaoyue & Yang, Zhanxin, 2012. "Chinese lexical networks: The structure, function and formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(21), pages 5254-5263.
    17. Yao, Xin & Zhang, Chang-shui & Chen, Jin-wen & Li, Yan-da, 2005. "On the formation of degree and cluster-degree correlations in scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 661-673.
    18. Bian, Tian & Hu, Jiantao & Deng, Yong, 2017. "Identifying influential nodes in complex networks based on AHP," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 422-436.
    19. Lian, Ying & Dong, Xuefan & Liu, Yijun, 2017. "Topological evolution of the internet public opinion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 567-578.
    20. Zhao, Star X. & Rousseau, Ronald & Ye, Fred Y., 2011. "h-Degree as a basic measure in weighted networks," Journal of Informetrics, Elsevier, vol. 5(4), pages 668-677.

    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:437:y:2015:i:c:p:36-54. 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.