IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v566y2021ics0378437120309298.html
   My bibliography  Save this article

Inferring pattern generators on networks

Author

Listed:
  • Nyczka, Piotr
  • Hütt, Marc-Thorsten
  • Lesne, Annick

Abstract

Given a pattern on a network, i.e. a subset of nodes, can we assess, whether they are randomly distributed on the network or have been generated in a systematic fashion following the network architecture? This question is at the core of network-based data analyses across a range of disciplines — from incidents of infection in social networks to sets of differentially expressed genes in biological networks. Here we introduce generic ‘pattern generators’ based on an Eden growth model. We assess the capacity of different pattern measures like connectivity, edge density or various average distances, to infer the parameters of the generator from the observed patterns. Some measures perform consistently better than others in inferring the underlying pattern generator, while the best performing measures depend on the global topology of the underlying network. Moreover, we show that pattern generator inference remains possible in case of limited visibility of the patterns.

Suggested Citation

  • Nyczka, Piotr & Hütt, Marc-Thorsten & Lesne, Annick, 2021. "Inferring pattern generators on networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    • Handle: RePEc:eee:phsmap:v:566:y:2021:i:c:s0378437120309298
    DOI: 10.1016/j.physa.2020.125631
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120309298
    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.2020.125631?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. Marcin Rybak & Krzysztof Kułakowski, 2013. "Competing Contact Processes On Homogeneous Networks With Tunable Clusterization," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 24(03), pages 1-13.
    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.

      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:566:y:2021:i:c:s0378437120309298. 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.