IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v361y2019icp747-751.html
   My bibliography  Save this article

Book embedding of complex network with community structure

Author

Listed:
  • Zhao, Bin
  • Chen, Wengu
  • Meng, Jixiang
  • Liu, Fengxia

Abstract

Community structure provides useful information for understanding the organization structure of a network and the interaction between different functional modules, as a result, it has been a hot research topic in the study of complex network. In graph theory, a complex network with a community structure is a stochastic block model (SBM). The stochastic block model is a generative model for random graph, and it is widely used in daily life. The book embedding plays an important role in computer science. The main purpose of this paper is to give the book embedding of the stochastic block model by Lovász Local Lemma. This is the first work about book embedding of random graph, and the difference between the upper and lower bounds of the page number of G∈G(n,k,p,q) is only one if p=1.

Suggested Citation

  • Zhao, Bin & Chen, Wengu & Meng, Jixiang & Liu, Fengxia, 2019. "Book embedding of complex network with community structure," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 747-751.
    • Handle: RePEc:eee:apmaco:v:361:y:2019:i:c:p:747-751
    DOI: 10.1016/j.amc.2019.06.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300319304850
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2019.06.020?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.

    Citations

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


    Cited by:

    1. Xu, Helian & Feng, Lianyue & Wu, Gang & Zhang, Qi, 2021. "Evolution of structural properties and its determinants of global waste paper trade network based on temporal exponential random graph models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    2. Lianyue Feng & Helian Xu & Gang Wu & Wenting Zhang, 2021. "Service trade network structure and its determinants in the Belt and Road based on the temporal exponential random graph model," Pacific Economic Review, Wiley Blackwell, vol. 26(5), pages 617-650, December.

    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:apmaco:v:361:y:2019:i:c:p:747-751. 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: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.