IDEAS home Printed from https://ideas.repec.org/a/wsi/ijmpcx/v27y2016i04ns0129183116500467.html
   My bibliography  Save this article

Community structure detection based on the neighbor node degree information

Author

Listed:
  • Li-Ying Tang

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China)

  • Sheng-Nan Li

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China)

  • Jian-Hong Lin

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China)

  • Qiang Guo

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China)

  • Jian-Guo Liu

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China†Data Science and Cloud Service Research Centre, Shanghai University of Finance and Economics, Shanghai 200433, P. R. China)

Abstract

Community structure detection is of great significance for better understanding the network topology property. By taking into account the neighbor degree information of the topological network as the link weight, we present an improved Nonnegative Matrix Factorization (NMF) method for detecting community structure. The results for empirical networks show that the largest improved ratio of the Normalized Mutual Information value could reach 63.21%. Meanwhile, for synthetic networks, the highest Normalized Mutual Information value could closely reach 1, which suggests that the improved method with the optimal λ can detect the community structure more accurately. This work is helpful for understanding the interplay between the link weight and the community structure detection.

Suggested Citation

  • Li-Ying Tang & Sheng-Nan Li & Jian-Hong Lin & Qiang Guo & Jian-Guo Liu, 2016. "Community structure detection based on the neighbor node degree information," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(04), pages 1-11, April.
    • Handle: RePEc:wsi:ijmpcx:v:27:y:2016:i:04:n:s0129183116500467
    DOI: 10.1142/S0129183116500467
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0129183116500467
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0129183116500467?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. Yan, Chao & Chang, Zhenhai, 2020. "Modularized convex nonnegative matrix factorization for community detection in signed and unsigned networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    2. Yang, Kai & Guo, Qiang & Liu, Jian-Guo, 2018. "Community detection via measuring the strength between nodes for dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 256-264.
    3. Ioannidis, Evangelos & Varsakelis, Nikos & Antoniou, Ioannis, 2018. "Experts in Knowledge Networks: Central Positioning and Intelligent Selections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 890-905.
    4. Li, Sheng-Nan & Guo, Qiang & Yang, Kai & Liu, Jian-Guo & Zhang, Yi-Cheng, 2018. "Uncovering the popularity mechanisms for Facebook applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 422-429.
    5. Zhao, Zi-Juan & Guo, Qiang & Yu, Kai & Liu, Jian-Guo, 2020. "Identifying influential nodes for the networks with community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    6. Sun, Hong-liang & Ch’ng, Eugene & Yong, Xi & Garibaldi, Jonathan M. & See, Simon & Chen, Duan-bing, 2018. "A fast community detection method in bipartite networks by distance dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 108-120.

    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:wsi:ijmpcx:v:27:y:2016:i:04:n:s0129183116500467. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijmpc/ijmpc.shtml .

    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.