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Nonuniform Hyper-Network Embedding with Dual Mechanism

Published: 05 May 2020 Publication History
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  • Abstract

    Network embedding which aims to learn the low-dimensional representations for vertices in networks has been extensively studied in recent years. Although there are various models designed for networks with different properties and different structures for different tasks, most of them are only applied to normal networks which only contain pairwise relationships between vertices. In many realistic cases, relationships among objects are not pairwise and such relationships can be better modeled by a hyper-network in which each edge can connect an uncertain number of vertices. In this article, we focus on two properties of hyper-networks: nonuniform and dual property. In order to make full use of these two properties, we firstly propose a flexible model called Hyper2vec to learn the embeddings of hyper-networks by applying a biased second order random walk strategy to hyper-networks in the framework of Skip-gram. Then, we combine the features of hyperedges by considering the dual hyper-networks to build a further model called NHNE based on 1D convolutional neural networks, and train a tuplewise similarity function for the nonuniform relationships in hyper-networks. Extensive experiments demonstrate the significant effectiveness of our methods for hyper-network embedding.

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    Published In

    cover image ACM Transactions on Information Systems
    ACM Transactions on Information Systems  Volume 38, Issue 3
    July 2020
    311 pages
    ISSN:1046-8188
    EISSN:1558-2868
    DOI:10.1145/3394096
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 05 May 2020
    Accepted: 01 March 2020
    Revised: 01 March 2020
    Received: 01 July 2019
    Published in TOIS Volume 38, Issue 3

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    Author Tags

    1. Network embedding
    2. dual network
    3. hyper-network
    4. link prediction

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    • Research-article
    • Research
    • Refereed

    Funding Sources

    • CCF Opening Project of Information System
    • National Key Research and Development Program
    • National Natural Science Foundation of China
    • Natural Science Foundation of Guangdong
    • Program for Science and Technology Planning Project of Guangdong Province of China

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