IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v502y2018icp106-122.html
   My bibliography  Save this article

Reconstructing networks from dynamics with correlated noise

Author

Listed:
  • Tam, H.C.
  • Ching, Emily S.C.
  • Lai, Pik-Yin

Abstract

Reconstructing the structure of complex networks from measurements of the nodes is a challenge in many branches of science. External influences are always present and act as a noise to the networks of interest. In this paper, we present a method for reconstructing networks from measured dynamics of the nodes subjected to correlated noise that cannot be approximated by a white noise. This method can reconstruct the links of both bidirectional and directed networks, the correlation time and strength of the noise, and also the relative coupling strength of the links when the coupling functions have certain properties. Our method is built upon theoretical relations between network structure and measurable quantities from the dynamics that we have derived for systems that have fixed point dynamics in the noise-free limit. Using these theoretical results, we can further explain the shortcomings of two common practices of inferring links for bidirectional networks using the Pearson correlation coefficient and the partial correlation coefficient.

Suggested Citation

  • Tam, H.C. & Ching, Emily S.C. & Lai, Pik-Yin, 2018. "Reconstructing networks from dynamics with correlated noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 106-122.
    • Handle: RePEc:eee:phsmap:v:502:y:2018:i:c:p:106-122
    DOI: 10.1016/j.physa.2018.02.166
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118302498
    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.2018.02.166?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. Robert J Prill & Robert Vogel & Guillermo A Cecchi & Grégoire Altan-Bonnet & Gustavo Stolovitzky, 2015. "Noise-Driven Causal Inference in Biomolecular Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
    2. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    3. Matthieu Gilson & Ruben Moreno-Bote & Adrián Ponce-Alvarez & Petra Ritter & Gustavo Deco, 2016. "Estimation of Directed Effective Connectivity from fMRI Functional Connectivity Hints at Asymmetries of Cortical Connectome," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-30, March.
    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. Emerson, Isaac Arnold & Amala, Arumugam, 2017. "Protein contact maps: A binary depiction of protein 3D structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 782-791.
    2. Faedo, Nicolás & García-Violini, Demián & Ringwood, John V., 2021. "Controlling synchronization in a complex network of nonlinear oscillators via feedback linearisation and H∞-control," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    3. Xiao‐Bing Hu & Hang Li & XiaoMei Guo & Pieter H. A. J. M. van Gelder & Peijun Shi, 2019. "Spatial Vulnerability of Network Systems under Spatially Local Hazards," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 162-179, January.
    4. 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.
    5. Igor Belykh & Mateusz Bocian & Alan R. Champneys & Kevin Daley & Russell Jeter & John H. G. Macdonald & Allan McRobie, 2021. "Emergence of the London Millennium Bridge instability without synchronisation," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    6. Berahmand, Kamal & Bouyer, Asgarali & Samadi, Negin, 2018. "A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 41-54.
    7. Zhang, Yun & Liu, Yongguo & Li, Jieting & Zhu, Jiajing & Yang, Changhong & Yang, Wen & Wen, Chuanbiao, 2020. "WOCDA: A whale optimization based community detection algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    8. Soh, Harold & Lim, Sonja & Zhang, Tianyou & Fu, Xiuju & Lee, Gary Kee Khoon & Hung, Terence Gih Guang & Di, Pan & Prakasam, Silvester & Wong, Limsoon, 2010. "Weighted complex network analysis of travel routes on the Singapore public transportation system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5852-5863.
    9. Wang, Qingyun & Duan, Zhisheng & Chen, Guanrong & Feng, Zhaosheng, 2008. "Synchronization in a class of weighted complex networks with coupling delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(22), pages 5616-5622.
    10. De Montis, Andrea & Ganciu, Amedeo & Cabras, Matteo & Bardi, Antonietta & Mulas, Maurizio, 2019. "Comparative ecological network analysis: An application to Italy," Land Use Policy, Elsevier, vol. 81(C), pages 714-724.
    11. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    12. T. Botmart & N. Yotha & P. Niamsup & W. Weera, 2017. "Hybrid Adaptive Pinning Control for Function Projective Synchronization of Delayed Neural Networks with Mixed Uncertain Couplings," Complexity, Hindawi, vol. 2017, pages 1-18, August.
    13. Sgrignoli, P. & Agliari, E. & Burioni, R. & Schianchi, A., 2015. "Instability and network effects in innovative markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 108(C), pages 260-271.
    14. Long Ma & Xiao Han & Zhesi Shen & Wen-Xu Wang & Zengru Di, 2015. "Efficient Reconstruction of Heterogeneous Networks from Time Series via Compressed Sensing," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-12, November.
    15. Wu, Tianyu & Huang, Xia & Chen, Xiangyong & Wang, Jing, 2020. "Sampled-data H∞ exponential synchronization for delayed semi-Markov jump CDNs: A looped-functional approach," Applied Mathematics and Computation, Elsevier, vol. 377(C).
    16. Liang’an Huo & Fan Ding & Chen Liu & Yingying Cheng, 2018. "Dynamical Analysis of Rumor Spreading Model considering Node Activity in Complex Networks," Complexity, Hindawi, vol. 2018, pages 1-10, November.
    17. Yang, Hyeonchae & Jung, Woo-Sung, 2016. "Structural efficiency to manipulate public research institution networks," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 21-32.
    18. Zhu, Mixin & Zhou, Xiaojun, 2023. "Hybrid opportunistic maintenance policy for serial-parallel multi-station manufacturing systems with spare part overlap," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    19. Ye, Dan & Yang, Xiang & Su, Lei, 2017. "Fault-tolerant synchronization control for complex dynamical networks with semi-Markov jump topology," Applied Mathematics and Computation, Elsevier, vol. 312(C), pages 36-48.
    20. Dragicevic, Arnaud Z. & Sinclair-Desgagné, Bernard, 2013. "Sustainable network dynamics," Ecological Modelling, Elsevier, vol. 270(C), pages 43-53.

    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:502:y:2018:i:c:p:106-122. 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.