High-Order Joint Embedding for Multi-Level Link Prediction

• Published in: Journal of the American Statistical Association Vol. 118; no. 543; pp. 1692 - 1706
• Main Authors: Yuan, Yubai, Qu, Annie
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis 03-07-2023; Taylor & Francis Ltd
• Subjects: Algorithms; Convergence; Data augmentation; Dependency; Ego; Embedding; Encoding; Graph representations; Graphical representations; Hypergraph; Links; Network analysis; Network dependency; Shared parameters; Simulation; Statistics; Subgroups; Symmetric tensor completion; Tensors
• ISSN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2021.2005608

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.
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