IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v13y2021i3p64-d510680.html
   My bibliography  Save this article

A Classification Method for Academic Resources Based on a Graph Attention Network

Author

Listed:
  • Jie Yu

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Yaliu Li

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Chenle Pan

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Junwei Wang

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

Abstract

Classification of resource can help us effectively reduce the work of filtering massive academic resources, such as selecting relevant papers and focusing on the latest research by scholars in the same field. However, existing graph neural networks do not take into account the associations between academic resources, leading to unsatisfactory classification results. In this paper, we propose an Association Content Graph Attention Network (ACGAT), which is based on the association features and content attributes of academic resources. The semantic relevance and academic relevance are introduced into the model. The ACGAT makes full use of the association commonality and the influence information of resources and introduces an attention mechanism to improve the accuracy of academic resource classification. We conducted experiments on a self-built scholar network and two public citation networks. Experimental results show that the ACGAT has better effectiveness than existing classification methods.

Suggested Citation

  • Jie Yu & Yaliu Li & Chenle Pan & Junwei Wang, 2021. "A Classification Method for Academic Resources Based on a Graph Attention Network," Future Internet, MDPI, vol. 13(3), pages 1-16, March.
  • Handle: RePEc:gam:jftint:v:13:y:2021:i:3:p:64-:d:510680
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/13/3/64/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/13/3/64/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fabian Meyer-Brötz & Edgar Schiebel & Leo Brecht, 2017. "Experimental evaluation of parameter settings in calculation of hybrid similarities: effects of first- and second-order similarity, edge cutting, and weighting factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1307-1325, June.
    2. Mingxuan Che & Kui Yao & Chao Che & Zhangwei Cao & Fanchen Kong, 2021. "Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism," Future Internet, MDPI, vol. 13(1), pages 1-10, January.
    3. Xiangpeng Song & Hongbin Yang & Congcong Zhou, 2019. "Pedestrian Attribute Recognition with Graph Convolutional Network in Surveillance Scenarios," Future Internet, MDPI, vol. 11(11), pages 1-13, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Salvatore Graziani & Maria Gabriella Xibilia, 2020. "Innovative Topologies and Algorithms for Neural Networks," Future Internet, MDPI, vol. 12(7), pages 1-4, July.
    2. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "Hybrid self-optimized clustering model based on citation links and textual features to detect research topics," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-21, October.
    3. Guadalupe Palacios-Núñez & Gabriel Vélez-Cuartas & Juan D. Botero, 2018. "Developmental tendencies in the academic field of intellectual property through the identification of invisible colleges," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(3), pages 1561-1574, June.
    4. Kerang Cao & Jingyu Gao & Kwang-nam Choi & Lini Duan, 2020. "Learning a Hierarchical Global Attention for Image Classification," Future Internet, MDPI, vol. 12(11), pages 1-11, October.
    5. Klaus Kammerer & Manuel Göster & Manfred Reichert & Rüdiger Pryss, 2021. "Ambalytics: A Scalable and Distributed System Architecture Concept for Bibliometric Network Analyses," Future Internet, MDPI, vol. 13(8), pages 1-29, August.
    6. Francesco Piccialli & Vincenzo Schiano Cola & Fabio Giampaolo & Salvatore Cuomo, 2021. "The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic," Information Systems Frontiers, Springer, vol. 23(6), pages 1467-1497, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:13:y:2021:i:3:p:64-:d:510680. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.