IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v658y2025ics0378437124007842.html
   My bibliography  Save this article

Node clustering in complex networks based on structural similarity

Author

Listed:
  • Feng, Deyue
  • Li, Meizhu
  • Zhang, Qi

Abstract

In the structural analysis of complex networks, node clustering is a method that identifying nodes with the same function or structural properties, which is also a part of the community detection. In this work, based on the structural similarity of nodes and the k-means++ algorithm, a new method of node clustering is proposed. This method can easily divide the hub nodes and peripheral nodes in the network with a core-peripheral structure into two sets. We also find that the changes in the number of nodes in different classes is a manifestation of the rules that guide the growth of the network under different conditions. Specifically, when the network’s growth follows the Erdős–Rényi model, the cluster of nodes is homogeneous. When the network’s growth adheres to the Barabási–Albert model, the peripheral nodes are the majority, and this trend will not change with the growing network size. All the results show that the clustering of nodes in the networks based on the nodes’ structural similarity can be used as a new method for research on the structural analysis of complex networks.

Suggested Citation

  • Feng, Deyue & Li, Meizhu & Zhang, Qi, 2025. "Node clustering in complex networks based on structural similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 658(C).
    • Handle: RePEc:eee:phsmap:v:658:y:2025:i:c:s0378437124007842
    DOI: 10.1016/j.physa.2024.130274
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124007842
    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.2024.130274?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.

    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:658:y:2025:i:c:s0378437124007842. 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: 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.