CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature
CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature
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DOI：
10.1002/asi.20317
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发表时间：
2006-02-01
影响因子：
--
通讯作者：
Chen, CM
中科院分区：
文献类型：
--
作者：
Chen, CM
本文介绍了一种通用方法的最新发展，用于检测和可视化科学文献中的新兴趋势和瞬态模式。这项工作为知识领域的可视化做出了重要的理论和方法贡献。专业被概念化和可视化为信息科学中两个基本概念之间的时变二元性：研究前沿和知识基础。研究前沿被定义为概念和潜在研究问题的新兴和短暂的分组。一个研究前沿的知识基础是它在科学文献中的引用和共同引用足迹，一个不断发展的被研究前沿概念引用的科学出版物网络。Kleinberg（2002）的突发检测算法适用于识别新兴的研究前沿概念。Freeman（1979）的介数中心性度量被用来突出随时间推移的范式转变的潜在关键点。设计并实现了两种互补的可视化视图：聚类视图和时区视图。该方法的贡献是：（a）知识基础的性质是由新兴的研究前沿术语在算法和时间上确定的，（B）共同引用集群的价值是明确解释的研究前沿概念，（c）视觉上突出和算法检测的关键点，大大降低了可视化网络的复杂性。在CiteSpace II，Java应用程序中实现的建模和可视化过程，并应用到两个研究领域的分析：大规模灭绝（1981-2004年）和恐怖主义（1990-2003年）。可视化网络中的突出趋势和关键点是与领域专家合作验证的，他们是关键点文章的作者。工作的实际影响进行了讨论。确定了一些未来研究的挑战和机遇。
This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science: research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature an evolving network of scientific publications cited by research-front concepts. Kleinberg's (2002) burst detection algorithm is adapted to identify emergent research-front concepts. Freeman's (1979) betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are that (a) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, (b) the value of a co-citation cluster is explicitly interpreted in terms of research-front concepts, and (c) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981-2004) and terrorism (1990-2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.