High-Order Joint Embedding for Multi-Level Link Prediction

Link prediction infers potential links from observed networks, and is one of the essential problems in network analyses. In contrast to traditional graph representation modeling which only predicts two-way pairwise relations, we propose a novel tensor-based joint network embedding approach on simultaneously encoding pairwise links and hyperlinks onto a latent space, which captures the dependency between pairwise and multi-way links in inferring potential unobserved hyperlinks. The major advantage of the proposed embedding procedure is that it incorporates both the pairwise relationships and subgroup-wise structure among nodes to capture richer network information. In addition, the proposed method introduces a hierarchical dependency among links to infer potential hyperlinks, and leads to better link prediction. In theory we establish the estimation consistency for the proposed embedding approach, and provide a faster convergence rate compared to link prediction using pairwise links or hyperlinks only. Numerical studies on both simulation settings and Facebook ego-networks indicate that the proposed method improves both hyperlink and pairwise link prediction accuracy compared to existing link prediction algorithms. Supplementary materials for this article are available online.

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 2022-01-05, available online: https://www.tandfonline.com/10.1080/01621459.2021.2005608.

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Work Title High-Order Joint Embedding for Multi-Level Link Prediction
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Open Access
Creators
  1. Yubai Yuan
  2. Annie Qu
Keyword
  1. Data augmentation
  2. Hypergraph
  3. Latent factor model
  4. Method of moments
  5. Nonconvex optimization
  6. Shared parameters
  7. Symmetric tensor completion
License CC BY-NC 4.0 (Attribution-NonCommercial)
Work Type Article
Publisher
  1. Journal of the American Statistical Association
Publication Date January 5, 2022
Publisher Identifier (DOI)
  1. https://doi.org/10.1080/01621459.2021.2005608
Deposited January 23, 2024

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  • Added main_text-1.pdf
  • Added Creator Yubai Yuan
  • Added Creator Annie Qu
  • Published
  • Updated Keyword Show Changes
    Keyword
    • Data augmentation, Hypergraph, Latent factor model, Method of moments, Nonconvex optimization, Shared parameters, Symmetric tensor completion
  • Updated