IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v613y2023ics0378437123000821.html
   My bibliography  Save this article

Discovering block structure in networks

Author

Listed:
  • Arthur, Rudy

Abstract

A generalisation of modularity, called block modularity, is defined. This is a quality function which evaluates a label assignment against an arbitrary block pattern. Therefore, unlike standard modularity or its variants, arbitrary network structures can be compared and an optimal block matrix can be determined. Some simple algorithms for optimising block modularity are described and applied on networks with planted structure. In many cases the planted structure is recovered. Cases where it is not are analysed and it is found that strong degree-correlations explain the planted structure so that the discovered pattern is more ‘surprising’ than the planted one under the configuration model. Some well studied networks are analysed with this new method, which is found to automatically deconstruct the network in a very useful way for creating a summary of its key features.

Suggested Citation

  • Arthur, Rudy, 2023. "Discovering block structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    • Handle: RePEc:eee:phsmap:v:613:y:2023:i:c:s0378437123000821
    DOI: 10.1016/j.physa.2023.128527
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437123000821
    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.2023.128527?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. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Arthur, Rudy, 2020. "Modularity and projection of bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    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.
    1. Hadi Vafaii & Francesca Mandino & Gabriel Desrosiers-Grégoire & David O’Connor & Marija Markicevic & Xilin Shen & Xinxin Ge & Peter Herman & Fahmeed Hyder & Xenophon Papademetris & Mallar Chakravarty , 2024. "Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    2. Neelu Chaudhary & Hardeo Kumar Thakur & Rinky Dwivedi, 2022. "An ensemble model to optimize modularity in dynamic bipartite networks," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2248-2260, October.

    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:613:y:2023:i:c:s0378437123000821. 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.