图书情报工作

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2025 , Vol. 69 >Issue 2: 96 - 107

DOI: https://doi.org/10.13266/j.issn.0252-3116.2025.02.009

研究论文

学者合作网络外部影响力与学术成就的关系研究

  • 闫晓慧 ,
  • 杨杰 ,
  • 杨文霞 ,
  • 王昊 ,
  • 邓三鸿
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  • 1 山西警察学院侦查系 太原 030401;
    2 南京大学信息管理学院 南京 210023;
    3 数据工程与知识服务省高校重点实验室(南京大学) 南京 210023
闫晓慧，讲师，博士；杨杰，博士研究生；杨文霞，博士研究生；王昊，教授，博士生导师；邓三鸿，教授，博士生导师，通信作者，E-mail：sanhong@nju.edu.cn。

收稿日期: 2024-01-26

  修回日期: 2024-05-24

  网络出版日期: 2025-01-25

基金资助

本文系国家社会科学基金项目“大数据环境下学术成果真实价值与影响的实时预测及长期评价研究”（项目编号：19BTQ062）研究成果之一。

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A Study on the Relationship Between Scholars’ Collaboration External Impact and Academic Success

  • Yan Xiaohui ,
  • Yang Alex Jie ,
  • Yang Wenxia ,
  • Wang Hao ,
  • Deng Sanhong
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  • 1 Investigation Department, Shanxi Police College, Taiyuan 030401;
    2 School of Information Management, Nanjing University, Nanjing 210023;
    3 Key Laboratory of Data Engineering and Knowledge Services in Provincial Universities (Nanjing University), Nanjing 210023
Yan Xiaohui, lecturer, PhD; Yang Alex Jie, doctoral candidate; Yang Wenxia, doctoral candidate; Wang Hao, professor, PhD, doctoral supervisor; Deng Sanhong, professor, PhD, doctoral supervisor, corresponding author, E-mail: sanhong@nju.edu.cn.

Received date: 2024-01-26

  Revised date: 2024-05-24

  Online published: 2025-01-25

Supported by

This work is supported by the National Social Science Fund of China project titled “Real-Time Prediction and Long-Term Evaluation of the True Value and Impact of Academic Achievements in the Big Data Environment”(Grant No.19BTQ062).

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摘要

[目的/意义] 传统计量指标在学者影响力评价时未考虑学术合作网络内外部的差异，本文提出合作网络外部被引量和外部 h 指数等学者合作网络外部影响力评价指标，为学者真实学术影响力评价提供依据。[方法/过程] 以美国物理学会数据集的 463 348 篇研究论文及其 234 086 位消歧学者的数据作为样本，从全局合作网络抽取学者的自我中心合作网络，基于学者合作网络对内部与外部进行区分，提出外部被引量和外部 h 指数等学者合作网络外部影响力评价指标，结合基准计量指标进行一致性检验，基于诺贝尔奖、沃尔夫奖和狄拉克奖等物理学奖项数据证明合作网络外部影响力评价指标的有效性和优越性。[结果/结论] 研究发现，学者合作网络内部与外部影响力分布存在显著差异，本文提出的学者合作网络外部影响力与基准指标一致性较强，收敛有效性分析表明外部被引量和外部 h 指数相比于基准指标能够更准确地识别获奖学者，表明合作网络外部影响力反映了学者的真实影响力，在学术成就中扮演着关键作用，本文提出的新指标对于构建未来人才评价体系和破“五唯”有着启示作用。

关键词： 科学计量; 人才评价; 合作网络; 外部影响力

本文引用格式

闫晓慧 , 杨杰 , 杨文霞 , 王昊 , 邓三鸿 . 学者合作网络外部影响力与学术成就的关系研究[J]. 图书情报工作, 2025 , 69(2) : 96 -107 . DOI: 10.13266/j.issn.0252-3116.2025.02.009

Abstract

[Purpose/Significance] Traditional bibliometric indicators fail to capture the distinctions between the internal and external dimensions of academic collaboration networks. This paper introduces new metrics, such as external citations and the external h-index, for evaluating the external impact of scholarly collaboration networks, providing a foundation for a more authentic assessment of scholars’ academic influence. [Method/Process] Utilizing data from the American Physical Society, which includes 463,348 research papers and 234,086 disambiguated authors, this study extracts scholars’ ego-centric collaboration networks from the global network. It distinguishes between internal and external influences within these networks and introduces metrics such as the external citation count and the external h-index. These are tested for consistency against traditional bibliometric indicators and validated using data from prestigious physics awards such as the Nobel Prize, the Wolf Prize, and the Dirac Prize, to demonstrate the effectiveness and superiority of the external impact metrics. [Result/Conclusion] The study reveals significant disparities in the distribution of internal versus external influence within scholarly collaboration networks. The proposed external impact metrics introduced are highly consistent with traditional indicators. Convergence validity analysis shows that the external citation count and external h-index are more precise in identifying award-winning scholars compared to traditional metrics. These findings suggest that the external influence of collaboration networks reflects the true impact of scholars and plays a crucial role in academic achievements. The new metrics proposed here offer insightful implications for constructing future talent evaluation systems and breaking away from the “five only” criteria, providing a pathway for reform in academic assessments.

Key words： scientometrics; scientists’ evaluation; collaborative network; external impact

参考文献

[1] LAZER D, PENTLAND A, WATTS D, et al. Computational social science: obstacles and opportunities [J]. Science, 2020, 369(6507): 1060-1062.
[2] 吴利俊, 侯鑫鑫, 张嵬. 多要素融合视域下的高校合作贡献度指数构建及实证研究[J]. 图书情报工作, 2024, 68(7): 113-122. (WU L J, HOU X X, ZHANG W. Construction and empirical research on the university cooperation contribution index from the perspective of multi-factor integration [J]. Library and information service, 2024, 68(7): 113-122.)
[3] FONTANAROSA P, BAUCHNER H, FLANAGIN A. Authorship and team science [J]. Jama-journal of the American Medical Association, 2017, 318(24): 2433-2437.
[4] LEDFORD H. Team science [J]. Nature, 2015, 525(7569): 308-311.
[5] 魏雅慧, 张琳, 黄颖. 合著关系与研究主题双重视角下的师承合作模式研究[J]. 图书情报工作, 2024, 68(6): 30-47. (WEI Y H, ZHANG L, HUANG Y. Research on the mentorship collaboration patterns from the dual perspectives of co-authorship relationships and research themes[J]. Library and information service, 2024, 68(6): 30-47.)
[6] FONTANA M, IORI M, MONTOBBIO F et al. New and atypicalcombinations: an assessment of novelty and interdisciplinarity [J]. Research policy, 2020, 49(7): 104063.
[7] VASILYEVA E, KOZLOV A, ALFARO-BITTNER K, et al. Multilayer representation of collaboration networks with higherorder interactions [J]. Scientific reports, 2021, 11(1): 5666.
[8] YANG Y, TIAN T Y, WOODRUFF T K, et al. Gender-diverse teams produce more novel and higher-impact scientific ideas [J]. Proceedings of the National Academy of Sciences of the United States of America, 2022, 119(36): e2200841119.
[9] LIU M, JAISWAL A, BU Y, et al. Team formation and team impact: the balance between team freshness and repeat collaboration [J]. Journal of informetrics, 2022, 16(4): 101337.
[10] SUN Y, LIVAN G, MA A, et al. Interdisciplinary researchers attain better long-term funding performance [J]. Communications physics, 2021, 4(1): 263.
[11] JONES B, WUCHTY S, UZZI B. Multi-university research teams: shifting impact, geography, and stratification in science [J]. Science, 2008, 322(5905): 1259-1262.
[12] PARISI G. Balance research funds across Europe [J]. Nature, 2016, 530(7588): 33-34.
[13] ZHOU J, ZENG A, FAN Y, et al. Identifying important scholars via directed scientific collaboration networks [J]. Scientometrics, 2018, 114(3): 1327-1343.
[14] WAGNER C, WHETSELL T, MUKHERJEE S. International research collaboration: novelty, conventionality, and atypicality in knowledge recombination [J]. Research policy, 2019, 48(5): 1260-1270.
[15] 唐旭丽, 李信. 科研团队多样性对学术颠覆性创新的影响研究——以人工智能领域为例[J]. 情报学报, 2023, 42(1): 43-58. (TANG X L, LI X. Effect of the diversity of scientific teams on disruptive innovation in academia: a case study in the field of artificial intelligence [J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(1): 43-58.)
[16] 吕冬晴, 阮选敏, 李江, 等. 跨学科知识融合对D指数的影响[J]. 情报学报, 2022, 41(3): 263-274. (LYU D Q, RUAN X M, LI J, et al. Research on the relationship between the interdisciplinarity and radicalness of papers from the perspective of knowledge integration [J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(3): 263-274.)
[17] FRONCZAK A, MROWINSKI M, FRONCZAK P. Scientific success from the perspective of the strength of weak ties [J]. Scientific reports, 2022, 12(1): 5074.
[18] PERIANES-RODRÍGUEZ A, OLMEDA-GÓMEZ C, MOYAANEGÓN F. Detecting, identifying and visualizing research groups in co-authorship networks [J]. Scientometrics, 2010, 82(2): 307-319.
[19] LI E, LIAO C, YEN H. Co-authorship networks and research impact: a social capital perspective [J]. Research policy, 2013, 42(9): 1515-1530.
[20] MARTIN-ALCAZAR F, RUIZ-MARTINEZ M, SANCHEZGARDEY G. Assessing social capital in academic research teams: a measurement instrument proposal [J]. Scientometrics, 2019, 121(2): 917-935.
[21] 吴柯烨, 孙建军, 谢紫悦. 基于专利文本挖掘的细粒度技术机会分析[J]. 情报学报, 2023, 42(10): 1199-1212. (WU K Y, SUN J J, XIE Z Y. Research on fine-grained technology opportunity analysis based on patent text mining [J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(10): 1199-1212.)
[22] 张金年, 罗艳. 基于内容的作者研究相似度与潜在合作网络分析——以图书馆学期刊为例[J]. 情报科学, 2021, 39(8): 86-93. (ZHANG J N, LUO Y. Content-based author research similarity and potential collaboration network analysis: taking library science journals as an example [J]. Information science, 2021, 39(8): 86-93.)
[23] PAUL A, DUTTA A. Community detection using local group assimilation [J]. Expert systems with applications, 2022, 206(Nov.): 117794.
[24] HU Z, LIN A, WILLETT P. Identification of researchcommunities in cited and uncited publications using a coauthorship network [J]. Scientometrics, 2019, 118(1): 1-19.
[25] CAI B, ZENG L, WANG Y, et al. Community detection method based on node density, degree centrality, and k-means clustering incomplex network [J]. Entropy, 2019, 21(12): 1145.
[26] CARUSI C, BIANCHI G. Scientificcommunity detection via bipartite scholar/journal graph co-clustering [J]. Journal of informetrics, 2019, 13(1): 354-386.
[27] HAN H, ZHAI X, HAN J, et al. Discovering research teams from scientific papers and patents[C]//Proceedings of the 3rd international conference oncomputer science and application engineering. New York: ACM, 2019: 1-5.
[28] 张靖雯, 闵超. 引文扩散视角下论文学术影响力和社会影响力比较研究——以生物医学为例[J]. 情报学报, 2023, 42(1): 31-42. (ZHANG J W, MIN C.Acomparative study of the academic and social impact of papers from the diffusion perspective: taking biomedicine as an example [J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(1): 31-42.)
[29] DING Y, CRONIN B. Popular and/or prestigious? measures of scholarly esteem [J]. Information processing & management, 2011, 47(1): 80-96.
[30] YIN Y, WANG Y, EVANS J A, et al. Quantifying the dynamics of failure across science, startups and security [J]. Nature, 2019, 575(7781): 190-194.
[31] MA A, MONDRAGÓN R J, LATORA V. Anatomy of funded research in science [J]. Proceedings of the National Academy of Sciences, 2015, 112(48): 14760-14765.
[32] YIN Y, GAO J, JONES B F, et al. Coevolution of policy and science during the pandemic [J]. Science, 2021, 371(6525): 128-130.
[33] WALTMAN L. A review of the literature on citation impact indicators [J]. Journal of informetrics, 2016, 10(2): 365-391.
[34] KUHN T. Historical structure of scientific discovery [J]. Science, 1962, 136(3518): 760-764.
[35] MIN C, BU Y, WU D, et al. Identifying citation patterns of scientific breakthroughs: a perspective of dynamic citation process [J]. Information processing & management, 2021, 58(1): 102428.
[36] 韩毅, 伍玉, 申东阳, 等. 中文科研论文未被引探索Ⅰ: 外部特征影响研究——以图书馆情报与文献学为例[J]. 图书情报工作, 2018, 62(4): 6-13. (HAN Y, WU Y, SHEN D Y, et al. PartⅠ of the exploration on uncited papers in Chinese: the influences of external features of papers: a case study of library and information science [J]. Library and information service, 2018, 62(4): 6-13.)
[37] BRITO R, NAVARRO A. The inconsistency of h -index: a mathematical analysis [J]. Journal of informetrics, 2021, 15(1): 101106.
[38] FONTANAROSA P, BAUCHNER H, FLANAGIN A. Authorship and team science [J]. JAMA, 2017, 318(24): 2433-2437.
[39] HEIDI L. Team science [J]. Nature, 2015, 525(7569): 308-311.
[40] MOHAMMED S, MORGAN A, NYANTAKYI E. On the influence of uncited publications on a researcher’s h-index [J]. Scientometrics, 2020, 122(3): 1791-1799.
[41] YIN Y, WANG Y, EVANS J A, et al. Quantifying the dynamics of failure across science, startups and security [J]. Nature, 2019, 575(7781): 190-194.
[42] SINATRA R, WANG D, DEVILLE P, et al. Quantifying the evolution of individual scientific impact [J]. Science, 2016, 354(6312): aaf5239.
[43] HICKS D, WOUTERS P, WALTMAN L, et al. The Leiden Manifesto for research metrics [J]. Nature, 2015, 520(7548): 429-431.
[44] WAY S, MORGAN A, LARREMORE D, Larremore D, et al. Productivity, prominence, and the effects of academic environment [J]. Proceedings of the National Academy of Sciences of the United States of America, 2019, 116(22): 10729-10733.
[45] CHU J, EVANS J. Slowed canonical progress in large fields of science [J]. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118(41): e2021636118.
[46] ALSTOTT J, BULLMORE E T, PLENZ D. Powerlaw: a python package for analysis of heavy-tailed distributions [J]. Plos one, 2014, 9(1): e85777.
[47] MERTON R. The Matthew effect in science. The reward andcommunication systems of science are considered [J]. Science, 1968, 159(3810): 56-63.
[48] ZHAI L, YAN X, ZHANG G. Bi-directional h-index: a new measure of node centrality in weighted and directed networks [J]. Journal of informetrics, 2018, 12(1): 299-314.
[49] BORNMANN L, TEKLES A. Convergent validity of several indicators measuring disruptiveness withmilestone assignments to physics papers by experts [J]. Journal of informetrics, 2021, 15(3): 101159.
[50] WANG R, ZHOU Y, ZENG A. Evaluating scientists by citation and disruption of their representative works [J]. Scientometrics, 2023, 128(3): 1689-1710.
[51] XU S, MARIANI M S, LU L, et al. Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data [J]. Journal of informetrics, 2020, 14(1): 101005.
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