中国物理B ›› 2024, Vol. 33 ›› Issue (3): 38901-038901.doi: 10.1088/1674-1056/ad181d

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A multilayer network diffusion-based model for reviewer recommendation

Yiwei Huang(黄羿炜)1,2, Shuqi Xu(徐舒琪)3, Shimin Cai(蔡世民)4,5,†, and Linyuan Lü(吕琳媛)6,1,2,‡   

  1. 1 Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China;
    2 Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China;
    3 Institute of Dataspace, Hefei Comprehensive National Science Center, Hefei 230088, China;
    4 Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China;
    5 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
    6 School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China
  • 收稿日期:2023-09-24 修回日期:2023-12-22 接受日期:2023-12-22 出版日期:2024-02-22 发布日期:2024-02-29
  • 通讯作者: Shimin Cai, Linyuan Lü E-mail:shimin.cai81@gmail.com;linyuan.lv@ustc.edu.cn
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No. T2293771) and the New Cornerstone Science Foundation through the XPLORER PRIZE.

A multilayer network diffusion-based model for reviewer recommendation

Yiwei Huang(黄羿炜)1,2, Shuqi Xu(徐舒琪)3, Shimin Cai(蔡世民)4,5,†, and Linyuan Lü(吕琳媛)6,1,2,‡   

  1. 1 Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China;
    2 Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China;
    3 Institute of Dataspace, Hefei Comprehensive National Science Center, Hefei 230088, China;
    4 Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China;
    5 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
    6 School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China
  • Received:2023-09-24 Revised:2023-12-22 Accepted:2023-12-22 Online:2024-02-22 Published:2024-02-29
  • Contact: Shimin Cai, Linyuan Lü E-mail:shimin.cai81@gmail.com;linyuan.lv@ustc.edu.cn
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No. T2293771) and the New Cornerstone Science Foundation through the XPLORER PRIZE.

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摘要： With the rapid growth of manuscript submissions, finding eligible reviewers for every submission has become a heavy task. Recommender systems are powerful tools developed in computer science and information science to deal with this problem. However, most existing approaches resort to text mining techniques to match manuscripts with potential reviewers, which require high-quality textual information to perform well. In this paper, we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network, with no requirement for textual information. The network incorporates the relationship of scholar-paper pairs, the collaboration among scholars, and the bibliographic coupling among papers. Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing, with improvements of over 7.62% in recall, 5.66% in hit rate, and 47.53% in ranking score. Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem, which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

关键词: reviewer recommendation, multilayer network, network diffusion model, recommender systems, complex networks

Abstract: With the rapid growth of manuscript submissions, finding eligible reviewers for every submission has become a heavy task. Recommender systems are powerful tools developed in computer science and information science to deal with this problem. However, most existing approaches resort to text mining techniques to match manuscripts with potential reviewers, which require high-quality textual information to perform well. In this paper, we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network, with no requirement for textual information. The network incorporates the relationship of scholar-paper pairs, the collaboration among scholars, and the bibliographic coupling among papers. Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing, with improvements of over 7.62% in recall, 5.66% in hit rate, and 47.53% in ranking score. Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem, which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

Key words: reviewer recommendation, multilayer network, network diffusion model, recommender systems, complex networks

中图分类号:  (Networks and genealogical trees)

  • 89.75.Hc
64.60.aq (Networks)