IDEAS home Printed from https://ideas.repec.org/a/oup/indcch/v31y2022i4p980-1003..html
   My bibliography  Save this article

Diffusion delay centrality: decelerating diffusion processes across networks

Author

Listed:
  • Valerio Leone Sciabolazza
  • Luca Riccetti

Abstract

This paper presents a new measure (the diffusion delay centrality—DDC) to identify agents who should be put into isolation to decelerate a diffusion process spreading throughout a network. We show that DDC assigns a high rank to agents acting as the gatekeepers of the fringe of the network. We also show that the ranking of nodes obtained from the DDC is predicted by the difference in the values of betweenness and eigenvector centrality of network agents. The findings presented might constitute a useful tool to reduce diffusion processes both for policy makers and for corporate managers in the organization of production.

Suggested Citation

  • Valerio Leone Sciabolazza & Luca Riccetti, 2022. "Diffusion delay centrality: decelerating diffusion processes across networks," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 31(4), pages 980-1003.
    • Handle: RePEc:oup:indcch:v:31:y:2022:i:4:p:980-1003.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/icc/dtab078
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    Citations

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


    Cited by:

    1. Zádor, Zsófia & Zhu, Zhen & Smith, Matthew & Gorgoni, Sara, 2022. "A weighted and normalized Gould–Fernandez brokerage measure," Greenwich Papers in Political Economy 37794, University of Greenwich, Greenwich Political Economy Research Centre.

    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:oup:indcch:v:31:y:2022:i:4:p:980-1003.. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/icc .

    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.