Searching for intellectual turning points: Progressive knowledge domain visualization
Searching for intellectual turning points: Progressive knowledge domain visualization
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DOI：
10.1073/pnas.0307513100
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发表时间：
2004-04-06
影响因子：
11.1
通讯作者：
Chen, CM
中科院分区：
文献类型：
--
作者：
Chen, CM
本文介绍了一种以前未描述的方法，逐步可视化知识领域的同引网络的演变。该方法首先从一系列等长的时间间隔切片中导出一系列共引网络。这些时间记录的网络被合并并以全景视图可视化，从而可以根据其视觉上的显着特征来识别具有智力意义的文章。该方法应用于理论物理中超弦场的共引研究。该研究的重点是搜索引发两次超弦革命的文章。识别全景图中视觉上显着的节点，并由该领域的领先科学家验证其智力贡献的性质。分析表明，对智力转折点的搜索可以缩小到可视化网络中视觉上显着的节点。该方法提供了一种有前途的方法，可以将认知要求较高的任务简化为搜索地标、支点和枢纽。
This article introduces a previously undescribed method progressively visualizing the evolution of a knowledge domain's cocitation network. The method first derives a sequence of cocitation networks from a series of equal-length time interval slices. These time-registered networks are merged and visualized in a panoramic view in such away that intellectually significant articles can be identified based on their visually salient features. The method is applied to a cocitation study of the superstring field in theoretical physics. The study focuses on the search of articles that triggered two superstring revolutions. Visually salient nodes in the panoramic view are identified, and the nature of their intellectual contributions is validated by leading scientists in the field. The analysis has demonstrated that a search for intellectual turning points can be narrowed down to visually salient nodes in the visualized network. The method provides a promising way to simplify otherwise cognitively demanding tasks to a search for landmarks, pivots, and hubs.