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Activity Number: 69 - Network Analysis
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313767
Title: High-Order Embedding for Hyperlink Network Prediction
Author(s): Yubai Yuan* and Annie Qu
Companies: University of Illinois at Urbana-Champaign and University of California Irvine
Keywords: hypergraph; method of moments; network embedding; non-convex optimization; shared parameters; symmetric tensor completion

Link prediction infers the potential links from the observed network, 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 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 the hierarchical dependency among links to infer potential hyperlinks, and leads to a better link prediction. In theory we establish the estimation consistency for the proposed embedding approach, and provide a faster converge rate compared to hyperlink prediction utilizing pairwise links only. Numerical studies on both simulations and Facebook ego-network indicate that the proposed method improves both hyperlink and pairwise link predictions accuracy compared to existing methods.

Authors who are presenting talks have a * after their name.

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