IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0108471.html
   My bibliography  Save this article

Estimation of Global Network Statistics from Incomplete Data

Author

Listed:
  • Catherine A Bliss
  • Christopher M Danforth
  • Peter Sheridan Dodds

Abstract

Complex networks underlie an enormous variety of social, biological, physical, and virtual systems. A profound complication for the science of complex networks is that in most cases, observing all nodes and all network interactions is impossible. Previous work addressing the impacts of partial network data is surprisingly limited, focuses primarily on missing nodes, and suggests that network statistics derived from subsampled data are not suitable estimators for the same network statistics describing the overall network topology. We generate scaling methods to predict true network statistics, including the degree distribution, from only partial knowledge of nodes, links, or weights. Our methods are transparent and do not assume a known generating process for the network, thus enabling prediction of network statistics for a wide variety of applications. We validate analytical results on four simulated network classes and empirical data sets of various sizes. We perform subsampling experiments by varying proportions of sampled data and demonstrate that our scaling methods can provide very good estimates of true network statistics while acknowledging limits. Lastly, we apply our techniques to a set of rich and evolving large-scale social networks, Twitter reply networks. Based on 100 million tweets, we use our scaling techniques to propose a statistical characterization of the Twitter Interactome from September 2008 to November 2008. Our treatment allows us to find support for Dunbar's hypothesis in detecting an upper threshold for the number of active social contacts that individuals maintain over the course of one week.

Suggested Citation

  • Catherine A Bliss & Christopher M Danforth & Peter Sheridan Dodds, 2014. "Estimation of Global Network Statistics from Incomplete Data," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-18, October.
  • Handle: RePEc:plo:pone00:0108471
    DOI: 10.1371/journal.pone.0108471
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0108471
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0108471&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0108471?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
    ---><---

    Citations

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


    Cited by:

    1. Mark D Humphries & Javier A Caballero & Mat Evans & Silvia Maggi & Abhinav Singh, 2021. "Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-22, July.
    2. Xue Cui & Lu Yang, 2024. "Systemic risk and idiosyncratic networks among global systemically important banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 58-75, January.
    3. Brunetti, Celso & Harris, Jeffrey H. & Mankad, Shawn & Michailidis, George, 2019. "Interconnectedness in the interbank market," Journal of Financial Economics, Elsevier, vol. 133(2), pages 520-538.
    4. Belik, Ivan & Knudsen, Eirik Sjåholm, 2023. "Link on, Link off: Data-driven management of organizational networks for ambidexterity," Journal of Business Research, Elsevier, vol. 157(C).

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0108471. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.