By continuing to use this site, you agree to the use of cookies in accordance with our privacy policy.

Science and Mathematics

High-Order Joint Embedding for Multi-Level Link Prediction

December 9, 2021 at 4:00pm5:00pm EST

Virtual (See event details)

This event has already occurred. The information may no longer be valid.

The Department of Mathematics in the College of Arts and Sciences is honored to welcome Dr. Yubai Yuan to deliver the weekly colloquium. Dr. Yuan is a Postdoctoral Researcher in Statistics at the University of California Irvine, where he works in complex network analysis, causal inference, and latent modeling.

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. This is a joint work with Prof. Annie Qu in UC-Irvine. 

Contact Leah Quinones for Zoom information.

This event was published on December 7, 2021.


Event Details