High-order joint embedding for multi-level link prediction
This work addresses link prediction in networks, particularly for hyperlinks, which is important for applications like social network analysis, but it appears incremental as it builds on existing embedding methods by incorporating hierarchical dependencies.
The paper tackles the problem of predicting multi-way hyperlinks in networks by proposing a tensor-based joint embedding that simultaneously encodes pairwise and hyperlink relationships, leading to improved prediction accuracy for both hyperlinks and pairwise links in simulations and Facebook ego-networks.
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.