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A Two-Stage Framework for Directed Hypergraph Link Prediction

Author

Listed:
  • 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)

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.

Suggested Citation

  • Guanchen Xiao & Jinzhi Liao & Zhen Tan & Xiaonan Zhang & Xiang Zhao, 2022. "A Two-Stage Framework for Directed Hypergraph Link Prediction," Mathematics, MDPI, vol. 10(14), pages 1-18, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2372-:d:856969
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    References listed on IDEAS

    as
    1. Nasiri, Elahe & Berahmand, Kamal & Li, Yuefeng, 2021. "A new link prediction in multiplex networks using topologically biased random walks," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
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