IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v605y2022ics0378437122006495.html
   My bibliography  Save this article

Two-stream signed directed graph convolutional network for link prediction

Author

Listed:
  • He, Changxiang
  • Zeng, Jiayuan
  • Li, Yan
  • Liu, Shuting
  • Liu, Lele
  • Xiao, Chen

Abstract

Using graph neural networks(GNNs) to transfer and enhance the richness of node information has played an important role in link prediction. However, most traditional GNNs only consider undirected graphs or unsigned graphs, which is limited for information extraction. In order to obtain a richer node representation, we propose a Two-Stream Signed Directed Graph Convolution Network(2S-SDGCN). We consider both the sign and direction when aggregating, which can characterize the topological structure information of the graph. To combine the information together, we conduct a new two-stream network, one for extracting latent factor and transfer pattern features, and the other for obtaining directional positive and negative features. We use five real social networks as datasets to verify the effectiveness of our model. These datasets are usually used as benchmarks to verify the effectiveness of signed network embedding. Experiments show that our model outperforms the existing methods.

Suggested Citation

  • He, Changxiang & Zeng, Jiayuan & Li, Yan & Liu, Shuting & Liu, Lele & Xiao, Chen, 2022. "Two-stream signed directed graph convolutional network for link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
  • Handle: RePEc:eee:phsmap:v:605:y:2022:i:c:s0378437122006495
    DOI: 10.1016/j.physa.2022.128036
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122006495
    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.2022.128036?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, Li & Wang, Jing & Fan, Dongmei & Zhang, Haifeng & Zhong, Kai, 2023. "A privacy preserving graph neural networks framework by protecting user’s attributes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(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:phsmap:v:605:y:2022:i:c:s0378437122006495. 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: 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.