High-order joint embedding for multi-level link prediction

TL;DR

This paper introduces a tensor-based joint embedding method for multi-level link prediction that captures complex network dependencies and improves prediction accuracy over traditional pairwise methods.

Contribution

It proposes a novel hierarchical tensor embedding approach that models both pairwise and multi-way links simultaneously, with theoretical guarantees and superior empirical performance.

Findings

Improved hyperlink and link prediction accuracy in simulations and real networks.

Theoretical proof of estimation consistency and faster convergence rates.

Outperforms existing algorithms in various network scenarios.

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 utilizing 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.