图书情报工作 ›› 2022, Vol. 66 ›› Issue (15): 97-106.DOI: 10.13266/j.issn.0252-3116.2022.15.010

• 情报研究 • 上一篇    下一篇

虚拟科研团队识别方法研究——以重症医学领域为例

吕千千1,2,3, 谭宗颖2   

  1. 1. 中国科学院大学经济与管理学院图书情报与档案管理系 北京 100049;
    2. 中国科学院文献情报中心 北京 100190;
    3. 中国电子技术标准化研究院 北京 100007
  • 收稿日期:2022-03-24 修回日期:2022-05-19 出版日期:2022-08-05 发布日期:2022-08-17
  • 作者简介:吕千千,博士,E-mail:lvqianqian@mail.las.ac.cn;谭宗颖,研究员,博士生导师。
  • 基金资助:
    本文系国家自然科学基金会应急管理项目"工程科技2035发展战略对基础研究的需求研究"(项目编号:L1624050)研究成果之一。

Research on the Identification Method of Virtual Scientific Research Team——Taking the Field of Critical Care Medicine as an Example

Lü Qianqian1,2,3, Tan Zongying2   

  1. 1. Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100049;
    2. Chinese Academy of Sciences, National Science Library, Beijing 100190;
    3. China Electronics Standardization Institute, Beijing 100007
  • Received:2022-03-24 Revised:2022-05-19 Online:2022-08-05 Published:2022-08-17

摘要: [目的/意义]在美国对华科技和人才封锁政策背景下,虚拟科研团队发现能够从情报研究的角度,为潜在合作对象识别、具有实体化潜力的科研团队发掘、团队组建及其结构优化提供参考和关键数据支撑,具有重要的理论和实践应用价值。[方法/过程]首先,研究选取Scopus数据库中2012-2021年重症医学领域获NIH资助的研究论文、会议论文和综述,引入并改进社团识别研究中基于模块度的Louvain算法,依据特定交叉科学领域科研人员科研产出合著网络中作者合作的紧密程度,初步识别虚拟科研团队;然后,结合向量空间模型、模拟退火算法和并行计算,进一步优化作者合作关系表征和精炼算法迭代过程,通过优化矩阵计算和矩阵输入提升虚拟科研团队识别的效果和效率;最后,对比GN算法和谱聚类算法的识别结果,验证改进的Louvain算法识别虚拟科研团队的有效性。[结果/结论]在大规模作者-作者关系矩阵运算中,将改进的Louvain算法用于识别特定领域的虚拟科研团队较为理想,具体表现在以下3个方面:①充分考虑情报学研究中作者对论文的贡献,提高科研团队识别效果;②通过优化矩阵输入,精炼局部模块度计算;增加并行计算,有效减少运算时间,提升识别效率;③虚拟科研团队识别结果为潜在合作对象的发现提供参考。

关键词: 虚拟科研团队, 作者关系矩阵, 科研团队识别

Abstract: [Purpose/Significance] Under the background of the US technology against China and talent blockade policy, the virtual scientific research team finds that from the perspective of intelligence research, it provides references and key data support for identifying potential cooperation objects, discovering scientific research teams with substantial potential, and forming a team and optimizing the structure, which has important theoretical and practical application value. [Method/Process] This study selected NIH-funded research papers, conference papers and reviews in the field of critical care medicine from the Scopus database from 2012 to 2021, introduced and improved the modularity-based Louvain algorithm in community identification research, and based on the closeness characteristics of author cooperation in the scientific research output co-authorship network of scientific researchers in a specific cross-scientific field, this paper initially identified of virtual scientific research teams; Then, combined with vector space model, simulated annealing algorithm and parallel computing, respectively, it further optimized the iterative process of author partnership characterization and refinement algorithm, through optimization matrix calculation and matrix input, it improved the effect and efficiency of virtual scientific research team identification; Finally, the identification results of GN algorithm and spectral clustering algorithm were compared to verify the effectiveness of the improved Louvain algorithm to identify virtual scientific research teams. [Result/Conclusion] In the calculation of large-scale author-author relationship matrix, it is ideal to use the improved Louvain algorithm to identify virtual scientific research teams in specific fields, it is manifested in the following three aspects. First, the contribution of authors to papers in information science research is fully considered, to improve the identification effect of the scientific research team. Second, by optimizing the matrix input, refining the local modularity calculation and increasing the parallel calculation, the operation time is effectively reduced, and the recognition efficiency is improved. Third, the identification results of the virtual scientific research team provide a reference for the discovery of potential partners.

Key words: virtual scientific research team, author relationship matrix, scientific research team identification

中图分类号: