A Two-Stage Framework for Directed Hypergraph Link Prediction

Guanchen Xiao, Jinzhi Liao, Zhen Tan, Xiaonan Zhang and Xiang Zhao
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Guanchen Xiao: Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
Jinzhi Liao: Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
Zhen Tan: Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
Xiaonan Zhang: Harbin Flight Academy, Harbin 150000, China
Xiang Zhao: Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China

Mathematics, 2022, vol. 10, issue 14, 1-18

Abstract: Hypergraphs, as a special type of graph, can be leveraged to better model relationships among multiple entities. In this article, we focus on the task of hyperlink prediction in directed hypergraphs, which finds a wide spectrum of applications in knowledge graphs, chem-informatics, bio-informatics, etc. Existing methods handling the task overlook the order constraints of the hyperlink’s direction and fail to exploit features of all entities covered by a hyperlink. To make up for the deficiency, we present a performant pipelined model, i.e., a two-stage framework for directed hyperlink prediction method (TF-DHP), which equally considers the entity’s contribution to the form of hyperlinks, and emphasizes not only the fixed order between two parts but also the randomness inside each part. The TF-DHP incorporates two tailored modules: a Tucker decomposition-based module for hyperlink prediction, and a BiLSTM-based module for direction inference. Extensive experiments on benchmarks—WikiPeople, JF17K, and ReVerb15K—demonstrate the effectiveness and universality of our TF-DHP model, leading to state-of-the-art performance.

Keywords: hyperlink prediction; hypergraph; Tucker decomposition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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