IDEAS home Printed from https://ideas.repec.org/a/wsi/ijmpcx/v21y2010i06ns012918311001549x.html
   My bibliography  Save this article

Similarity-Based Classification In Partially Labeled Networks

Author

Listed:
  • QIAN-MING ZHANG

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China)

  • MING-SHENG SHANG

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China)

  • LINYUAN LÜ

    (Department of Physics, University of Fribourg, Chemin du Musée 3, Fribourg CH-1700, Switzerland)

Abstract

Two main difficulties in the problem of classification in partially labeled networks are the sparsity of the known labeled nodes and inconsistency of label information. To address these two difficulties, we propose a similarity-based method, where the basic assumption is that two nodes are more likely to be categorized into the same class if they are more similar. In this paper, we introduce ten similarity indices defined based on the network structure. Empirical results on the co-purchase network of political books show that the similarity-based method can, to some extent, overcome these two difficulties and give higher accurate classification than the relational neighbors method, especially when the labeled nodes are sparse. Furthermore, we find that when the information of known labeled nodes is sufficient, the indices considering only local information can perform as good as those global indices while having much lower computational complexity.

Suggested Citation

  • Qian-Ming Zhang & Ming-Sheng Shang & Linyuan Lü, 2010. "Similarity-Based Classification In Partially Labeled Networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 21(06), pages 813-824.
    • Handle: RePEc:wsi:ijmpcx:v:21:y:2010:i:06:n:s012918311001549x
    DOI: 10.1142/S012918311001549X
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S012918311001549X
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S012918311001549X?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. Zhou, Yinzuo & Wu, Chencheng & Tan, Lulu, 2021. "Biased random walk with restart for link prediction with graph embedding method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    2. Haji Gul & Feras Al-Obeidat & Adnan Amin & Fernando Moreira & Kaizhu Huang, 2022. "Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    3. Le Li & Junyi Xu & Weidong Xiao & Bin Ge, 2016. "Behavior Based Social Dimensions Extraction for Multi-Label Classification," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-16, April.

    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:21:y:2010:i:06:n:s012918311001549x. 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.