High-Order Joint Embedding for Multi-Level Link Prediction

Abstract

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.

KEYWORDS:

• Data augmentation
• Hypergraph
• Network dependency
• Shared parameters
• Symmetric tensor completion

Acknowledgments

The authors thank the Associate Editor and two anonymous reviewers for their suggestions and helpful feedback which improved the paper significantly.

Funding

Supplementary Materials

The supplementary materials provide proofs of Theorem 4.1, Theorem 4.2, optimization algorithm in Section 3.5, explicit formulation for hyperlink modeling, detailed discussion on the theoretical results, detailed settings and results of simulations, and ROC graphs.

Additional information

Funding

This work is supported by NSF grants DMS 2019461 and DMS 1952406.