IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0255717.html
   My bibliography  Save this article

Overlapping community detection in networks based on link partitioning and partitioning around medoids

Author

Listed:
  • Alexander Ponomarenko
  • Leonidas Pitsoulis
  • Marat Shamshetdinov

Abstract

In this paper, we present a new method for detecting overlapping communities in networks with a predefined number of clusters called LPAM (Link Partitioning Around Medoids). The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph employing link partitioning and partitioning around medoids which are done through the use of a distance function defined on the set of nodes. We consider both the commute distance and amplified commute distance as distance functions. The performance of the LPAM method is evaluated with computational experiments on real life instances, as well as synthetic network benchmarks. For small and medium-size networks, the exact solution was found, while for large networks we found solutions with a heuristic version of the LPAM method.

Suggested Citation

  • Alexander Ponomarenko & Leonidas Pitsoulis & Marat Shamshetdinov, 2021. "Overlapping community detection in networks based on link partitioning and partitioning around medoids," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-43, August.
  • Handle: RePEc:plo:pone00:0255717
    DOI: 10.1371/journal.pone.0255717
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255717
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0255717&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0255717?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
    ---><---

    References listed on IDEAS

    as
    1. T. S. Evans & R. Lambiotte, 2010. "Line graphs of weighted networks for overlapping communities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(2), pages 265-272, September.
    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. Tyler J. Gandee & Sean C. Glaze & Philippe J. Giabbanelli, 2024. "A Visual Analytics Environment for Navigating Large Conceptual Models by Leveraging Generative Artificial Intelligence," Mathematics, MDPI, vol. 12(13), pages 1-23, June.

    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. Zhou, Xu & Liu, Yanheng & Wang, Jian & Li, Chun, 2017. "A density based link clustering algorithm for overlapping community detection in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 65-78.
    2. Badie, Reza & Aleahmad, Abolfazl & Asadpour, Masoud & Rahgozar, Maseud, 2013. "An efficient agent-based algorithm for overlapping community detection using nodes’ closeness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5231-5247.
    3. Dongxiao He & Di Jin & Carlos Baquero & Dayou Liu, 2014. "Link Community Detection Using Generative Model and Nonnegative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-10, January.
    4. Johannes Wachs & Mih'aly Fazekas & J'anos Kert'esz, 2019. "Corruption Risk in Contracting Markets: A Network Science Perspective," Papers 1909.08664, arXiv.org.
    5. Kire Trivodaliev & Aleksandra Bogojeska & Ljupco Kocarev, 2014. "Exploring Function Prediction in Protein Interaction Networks via Clustering Methods," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-16, June.
    6. Contreras-Aso, Gonzalo & Criado, Regino & Vera de Salas, Guillermo & Yang, Jinling, 2023. "Detecting communities in higher-order networks by using their derivative graphs," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    7. T. S. Evans & N. Hopkins & B. S. Kaube, 2012. "Universality of performance indicators based on citation and reference counts," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 473-495, November.
    8. Goetz, Stephan J. & Han, Yicheol, 2015. "Identifying Labor Market Areas Based on Link Communities," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 204870, Agricultural and Applied Economics Association.
    9. Criado-Alonso, Ángeles & Aleja, David & Romance, Miguel & Criado, Regino, 2022. "Derivative of a hypergraph as a tool for linguistic pattern analysis," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    10. Zhou, Xu & Liu, Yanheng & Zhang, Jindong & Liu, Tuming & Zhang, Di, 2015. "An ant colony based algorithm for overlapping community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 289-301.
    11. Iglesias Pérez, Sergio & Moral-Rubio, Santiago & Criado, Regino, 2021. "A new approach to combine multiplex networks and time series attributes: Building intrusion detection systems (IDS) in cybersecurity," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    12. Criado-Alonso, Ángeles & Battaner-Moro, Elena & Aleja, David & Romance, Miguel & Criado, Regino, 2021. "Enriched line graph: A new structure for searching language collocations," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    13. Zhenping Li & Xiang-Sun Zhang & Rui-Sheng Wang & Hongwei Liu & Shihua Zhang, 2013. "Discovering Link Communities in Complex Networks by an Integer Programming Model and a Genetic Algorithm," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-10, December.

    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:pone00:0255717. 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: 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.