期刊文献+

元网络视角下科研团队建模及分析 被引量:3

Modeling and Analyzing Research Team in the Perspective of Meta-Network
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摘要 认为对科学环境的研究正从科学图谱呈现阶段转向建模解释阶段,学术网络模型具有丰富的模型揭示能力。将科研团队视为人类社会系统,借助和扩展学术网络模型概念框架,根据多种科研团队相关学术网络模型的内在关联,融合构建出科研团队元网络模型,整合科研团队多维数据,全面揭示科研团队概貌。在此基础上,提出科研团队元网络模型的潜在静态分析应用方向,并进行实例分析,体现元网络模型的揭示能力。 The study of scientific environment is shifting from scientific mapping into modeling and explaining science.Scholarly network model has been proved to own rich revealing capabilities for science.The research team is considered as the human social system in this paper.Inheriting and expanding the conceptual framework of scholarly network,many sorts of networks related to research team are integrated into the meta-network model for research team,with the clue of their internal associations.In such a manner,multidimensional data is cooperated to gain a bird view upon research team.On this basis,the direction of the potential application for static analysis is thus proposed and empirical analysis is carried out as examples to reflect the revealing ability of such a meta-network for research team.
作者 李纲 毛进
出处 《图书情报工作》 CSSCI 北大核心 2014年第8期65-72,共8页 Library and Information Service
基金 国家自然科学基金项目"科研团队动态演化规律研究"(项目编号:71273196)研究成果之一
关键词 科研团队 元网络 网络模型 学术网络 research team meta-network network model scholarly network
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参考文献29

  • 1Morris S A, Goldstein M L. Manifestation of research teams in journal literature: A growth model of papers, authors, collaboration, coauthorship, weak ties, and Lotka's law[J]. Journal of the American Society for Information Science and Technology, 2007, 58(12): 1764-1782.
  • 2B?rner K, Boyack K W, Milojevi?S, et al. An introduction to modeling science: Basic model types, key definitions, and a general framework for the comparison of process models[M]//Models of Science Dynamics: Encounters between complexity theory and information sciences. Berlin: Springer, 2012: 3-22.
  • 3Scharnhorst A, B?rner K, van den Besselaar P. Models of science dynamics:Encounters between complexity theory and information sciences[M]. Berlin: Springer, 2012.
  • 4B?rner K, Chen Chaomei, Boyack K W. Visualizing knowledge domains[J]. Annual Review of Information Science and Technology, 2003, 37(1): 179-255.
  • 5Chen Chaomei. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature[J]. Journal of the American Society for Information Science and Technology, 2006, 57(3): 359-377.
  • 6Sci2 Team. Science of Science (Sci2) Tool[EB/OL].[2013-12-04].https://sci2.cns.iu.edu.
  • 7B?rner K, Maru J T, Goldstone R L. The simultaneous evolution of author and paper networks[J]. PNAS, 2004, 101(S1): 5266-5273.
  • 8Drott M C, Griffith B C. An empirical examination of Bradford's law and the scattering of scientific literature[J]. Journal of the American Society for Information Science, 1978, 29(5): 238-246.
  • 9Lotka A J. The frequency distribution of scientific productivity[J]. Journal of Washington Academy Sciences, 1926, 16(12):317-323.
  • 10Newman M E J. The structure of scientific collaboration networks[J]. PNAS, 2001, 98(2): 404-409.

二级参考文献15

  • 1McQuaker S. Relational Concept Knowledge in a Social Network[D].Ottawa:Dissertation for the Master Degree of the Carleton University,2007.
  • 2韦斯特;李建中;骆继洲.图论导引[M]北京:机械工业出版社,2006.
  • 3Newman M E J. The structure and function of complexnetworks[J].SIAM Review,2003,(02):167-256.doi:10.1137/S003614450342480.
  • 4何大韧;刘宗华;汪秉宏.复杂系统与复杂网络[M]北京:高等教育出版社,2009.
  • 5邱均平.信息计量学[M]武汉:武汉大学出版社,2007.
  • 6段宇锋.网络链接分析与网站评价研究[M]北京:北京图书馆出版社,2005.
  • 7王林;戴冠中.复杂网络的Scale-free性、Scale-free现象及其控制[M]北京:科学出版社,2009.
  • 8刘军.整体网分析讲义:UCINET软件实用指南[M]上海:格致出版社,2009.
  • 9汪小帆;李翔;陈关荣.复杂网络理论及其应用[M]北京:清华大学出版社,2006.
  • 10Watts D J,Strogatz S H. Collective dynamics of ‘small-world'networks[J].Nature,1998,(6684):440-442.

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