用沙普利方法确定科学合作网络的核心子团队
Lixin Zhou1,2, Chen Liu1,2, Xue Song2
1School of Intelligent Emergency Management, University of Shanghai for Science and Technology, Shanghai, China.
PeerJ. Computer science
|September 24, 2025
概括
本研究引入了一个新的框架,通过分析协作网络和研究成果来确定有效的科学子团队. 该方法有效地确定了高影响力的协作集群,以更好地评估和管理研究.
科学领域:
- 圣经计量学和科学计量学
- 网络科学 网络科学
- 研究评估研究评估
背景情况:
- 在科学合作中确定有效的子团队对于研究管理至关重要.
- 现有的方法往往侧重于个人影响或结构凝聚力,忽视了联合协作和输出.
- 在将网络结构与集体科学绩效相结合的方法中存在差距.
研究的目的:
- 开发一种新的二维框架,用于识别科学合作网络中的核心子团队.
- 整合网络拓与研究绩效指标,以进行全面的评估.
- 为确定高影响力协作集群提供一个工具.
主要方法:
- 提出了一个结合网络拓和研究绩效的框架.
- 使用Shapley值来衡量子团队的边际结构贡献.
- 雇佣子团队H指数来量化集体科学成果.
- 应用蒙特卡罗树搜索用于高贡献子团队的有效识别,包括对Shapley值的近似策略.
主要成果:
- 在61个现实世界科学合作团队中验证了该方法.
- 在识别具有高协作和引用影响的核心子团队方面表现出有效性.
- 实验结果证实了该方法能够精确地确定有影响力的研究团体.
结论:
- 拟议的框架成功地整合了结构和绩效指标,以确定关键的科学子团队.
- 为研究经理和资助机构提供了有价值的分析工具.
- 为需要综合指标的网络分析提供了可通用的方法.
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