IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v478y2017icp41-48.html
   My bibliography  Save this article

An improved algorithm for generalized community structure inference in complex networks

Author

Listed:
  • Qu, Yingfei
  • Shi, Weiren
  • Shi, Xin

Abstract

In recent years, the research of the community detection is not only on the structure that densely connected internally, but also on the structure of more patterns, such as heterogeneity, overlapping, core–periphery. In this paper, we build the network model based on the random graph models and propose an improved algorithm to infer the generalized community structures. We achieve it by introducing the generalized Bernstein polynomials and computing the latent parameters of vertices. The algorithm is tested both on the computer-generated benchmark networks and the real-world networks. Results show that the algorithm makes better performances on convergence speed and is able to discover the latent continuous structures in networks.

Suggested Citation

  • Qu, Yingfei & Shi, Weiren & Shi, Xin, 2017. "An improved algorithm for generalized community structure inference in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 41-48.
    • Handle: RePEc:eee:phsmap:v:478:y:2017:i:c:p:41-48
    DOI: 10.1016/j.physa.2017.02.039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117301838
    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.2017.02.039?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. Qu, Yingfei & Shi, Weiren & Shi, Xin, 2015. "Inferring overlapping community structure with degree-corrected block model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 48-54.
    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. Guijie Zhang & Luning Liu & Fangfang Wei, 2019. "Key nodes mining in the inventor–author knowledge diffusion network," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 721-735, March.

    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. Wu, Jianshe & Zhang, Long & Li, Yong & Jiao, Yang, 2016. "Partition signed social networks via clustering dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 568-582.
    2. Guijie Zhang & Luning Liu & Fangfang Wei, 2019. "Key nodes mining in the inventor–author knowledge diffusion network," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 721-735, March.

    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:478:y:2017:i:c:p:41-48. 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.