IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v162y2022ics0960077922006312.html
   My bibliography  Save this article

Cold-start link prediction integrating community information via multi-nonnegative matrix factorization

Author

Listed:
  • Tang, Minghu
  • Wang, Wenjun

Abstract

Cold-start link prediction has attracted much attention as a sub-problem of link prediction recently. However, due to the influence of some isolated nodes existing in the network, the network structure is disconnected, so that the existing methods cannot realize the task of link prediction well. Therefore, how to excavate and fuse some available information from the network data to help complete the link prediction is the key to solve this problem. In this paper, we propose a multi-nonnegative matrix factorization model that implements the prediction of missing edges of isolated nodes in the overall disconnected state of the network structure. Through several methods, three global and local attribute information, namely the community membership information of the node attributes, the attribute similarity between the nodes, and the partial first-order structure characteristics existing among the nodes, are extracted on network. Then, using the proposed new model, the cold-start link prediction problem on the structured disconnected network is finally solved by integrating the three kinds of information from multiple perspective. Extensive experiments demonstrate that our proposed method performs better than state-of-the-art methods when solving the cold-start link prediction problem.

Suggested Citation

  • Tang, Minghu & Wang, Wenjun, 2022. "Cold-start link prediction integrating community information via multi-nonnegative matrix factorization," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    • Handle: RePEc:eee:chsofr:v:162:y:2022:i:c:s0960077922006312
    DOI: 10.1016/j.chaos.2022.112421
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922006312
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2022.112421?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. Wu, Shun-yao & Zhang, Qi & Xue, Chuan-yu & Liao, Xi-yang, 2019. "Cold-start link prediction in multi-relational networks based on network dependence analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 558-565.
    2. Chen, Guangfu & Xu, Chen & Wang, Jingyi & Feng, Jianwen & Feng, Jiqiang, 2020. "Robust non-negative matrix factorization for link prediction in complex networks using manifold regularization and sparse learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    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. Yuliansyah, Herman & Othman, Zulaiha Ali & Bakar, Azuraliza Abu, 2023. "A new link prediction method to alleviate the cold-start problem based on extending common neighbor and degree centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).

    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. Lv, Laishui & Bardou, Dalal & Hu, Peng & Liu, Yanqiu & Yu, Gaohang, 2022. "Graph regularized nonnegative matrix factorization for link prediction in directed temporal networks using PageRank centrality," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    2. Yuliansyah, Herman & Othman, Zulaiha Ali & Bakar, Azuraliza Abu, 2023. "A new link prediction method to alleviate the cold-start problem based on extending common neighbor and degree centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).

    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:chsofr:v:162:y:2022:i:c:s0960077922006312. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.