IDEAS home Printed from https://ideas.repec.org/a/spr/cejnor/v32y2024i4d10.1007_s10100-023-00900-1.html
   My bibliography  Save this article

Closeness centrality reconstruction of tree graphs

Author

Listed:
  • Viktor Homolya

    (University of Szeged)

  • Tamás Vinkó

    (University of Szeged)

Abstract

This paper deals with a problem which belongs to the general question: how to reconstruct a graph from limited amount of information. As given information, we use the closeness centrality, which assigns a non-negative number to each node of the graph in question: the reciprocal of the sum of the length of the shortest paths between the node and all other nodes in the graph. Here we consider the case when the original graph is a tree and it is also known which nodes are the leaves. Based on some theoretical results, three algorithms are proposed. The first one aims at finding a non-exact solution $$G_P$$ G P in short time; the second one is a metaheuristic with some variants, they are intended to give further improvement on $$G_P$$ G P ; and the third one is designed for giving accurate results. Detailed explanations of these algorithms are given, together with numerical experiments to demonstrate their efficiency.

Suggested Citation

  • Viktor Homolya & Tamás Vinkó, 2024. "Closeness centrality reconstruction of tree graphs," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 32(4), pages 1061-1088, December.
    • Handle: RePEc:spr:cejnor:v:32:y:2024:i:4:d:10.1007_s10100-023-00900-1
    DOI: 10.1007/s10100-023-00900-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10100-023-00900-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10100-023-00900-1?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. Comellas, Francesc & Diaz-Lopez, Jordi, 2008. "Spectral reconstruction of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(25), pages 6436-6442.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Botond Bertok & Péter Biró & Marianna E.-Nagy, 2024. "Overview of Hungarian operations research based on the VOCAL 2022 conference," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 32(4), pages 897-902, December.

    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. Pandey, Pradumn Kumar & Badarla, Venkataramana, 2018. "Reconstruction of network topology using status-time-series data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 573-583.
    2. Maiorino, Enrico & Rizzi, Antonello & Sadeghian, Alireza & Giuliani, Alessandro, 2017. "Spectral reconstruction of protein contact networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 804-817.
    3. Lovato, Ilenia & Pini, Alessia & Stamm, Aymeric & Vantini, Simone, 2020. "Model-free two-sample test for network-valued data," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).

    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:spr:cejnor:v:32:y:2024:i:4:d:10.1007_s10100-023-00900-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.