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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
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    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. 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.
    3. 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.
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