A New Research Frame for Analyzing the Evolution of Research Topics Based on Co-word Network Communities

  • Cheng Qikai ,
  • Wang Xiaoguang
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  • School of Information Management, Wuhan University, Wuhan 430072

Received date: 2012-11-27

  Revised date: 2013-02-20

  Online published: 2013-04-20

Abstract

The knowledge structure of a discipline can be expressed by a co-word network. Six types of evolution of community are recognized, which are birth, death, splitting, merging, growth, and contraction. A topic evolution analysis framework is created based on Z-value mechanism and community match mechanism. Comparing with the traditional methods, the method proposed in this paper focus on the links between words rather than the frequency of words, which is a innovation in the field of ETD from the mesoscopical network view.

Cite this article

Cheng Qikai , Wang Xiaoguang . A New Research Frame for Analyzing the Evolution of Research Topics Based on Co-word Network Communities[J]. Library and Information Service, 2013 , (08) : 91 -96 . DOI: 10.7536/j.issn.0252-3116.2013.08.017

References

[1] Kontostathis A, Galitsky L M, Pottenger W M, et al. A survey of emerging trend detection in textual data mining[C]//Berry M.A Comprehensive Survey of Text Mining. Heidelberg: Springer-Verlag, 2003:185-224.
[2] 陈仕吉. 科学研究前沿探测方法综述[J],现代图书情报技术,2009(9):28-33.
[3] Pottenger W M, Yang Tinghao. Detecting emerging concepts in textual data mining[J]. Computational Information Retrieval, 2001,100(1): 89-105.
[4] Roy S, Gevry D, Pottenger W M. Methodologies for trend detection in textual data mining[C/OL].[2012-09-06].http://www.cse.lehigh.edu/~billp/pubs/ETDMethodologies.pdf.
[5] Le Minh-Hoang, Ho Tu-Bao, Nakamori Y. Detecting emerging trends from scientific corpora[J]. International Journal of Knowledge and Systems Sciences, 2005, 2(2): 63-69.
[6] Chen 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.
[7] Börner K, Chen Chaomei, Boyack K W. Visualizing knowledge domains[J].Annual Review of Information Science & Technology Cronin B. Inc./American Society for Information Science and Technology, 2003,37(1): 179-255.
[8] Wallace M L, Gingras Y, Duhon R. A new approach for detecting scientific specialties from raw cocitation networks[J]. Journal of the American Society for Information Science and Technology, 2009, 60 (2): 240-246.
[9] 殷蜀梅. 判断新兴研究趋势的技术框架研究[J]. 图书情报知识,2008(5):76-80.
[10] Tu Yining, Seng Jialang. Indices of novelty for emerging topic detection[J]. Journal of Information Processing and Management, 2012, 48(2): 303-325.
[11] 王晓光. 科学知识网络的结构与演化(I):共词网络方法的提出[J]. 情报学报, 2009, 28(4): 599-605.
[12] 王晓光. 科学知识网络的结构与演化(II):共词网络可视化与增长动力学[J]. 情报学报, 2010, 29(2): 314-322.
[13] Newman M E J, Girvan M. Finding and evaluating community structure in networks[J]. Physical Review E, 2004, 69(2): 026113.
[14] Boyack K W, Klavans R, Brner K. Mapping the backbone of science[J]. Scientometrics, 2005,64(3): 351-374.
[15] Lambiottea R, Panzarasab P. Communities, knowledge creation, and information diffusion[J]. Journal of Informetrics,2009,3(3): 180-190.
[16] Ding Ying. Community detection: Topological vs. Topical[J]. Journal of Informetrics, 2011,5(4): 498-514.
[17] Newman M.Analysis of weighted networks[J].The American Physical Society,2004,70(5):56-64.
[18] McCain K W. Assessing an author's influence using time series historic graphic mapping: The oeuvre of Conrad Hal Waddington (1905-1975)[J]. Journal of the American Society for Information Science and Technology, 2008, 59(4): 510–525.
[19] Guimer'a R, Sales-Pardo M, Amaral L A. Classes of complex networks defined by role-to-role connectivity profiles[J]. Nature Physics, 2007,3(1): 63-69.
[20] Palla G, Barabási A, Vicsek T. Quantifying social group evolution[J]. Nature, 2007, 446(7136):664-667.
[21] 钱铁云, 李青, 许承瑜. 面向科技主题发展分段的社区核心圈技术[J]. 计算机科学与探索, 2010(2):170-179.

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