Chen Chaomei
College of Information Science and Technology, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104-2875, USA.
Proc Natl Acad Sci U S A. 2004 Apr 6;101 Suppl 1(Suppl 1):5303-10. doi: 10.1073/pnas.0307513100. Epub 2004 Jan 14.
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 a way 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.
本文介绍了一种此前未被描述的方法,该方法可逐步可视化知识领域共被引网络的演变。该方法首先从一系列等长的时间间隔切片中导出共被引网络序列。这些记录了时间的网络被合并并以全景视图进行可视化,以便基于其视觉上显著的特征识别出具有重要学术意义的文章。该方法应用于理论物理中超弦领域的共被引研究。该研究聚焦于寻找引发两次超弦革命的文章。识别出全景视图中视觉上显著的节点,并由该领域的顶尖科学家验证其学术贡献的性质。分析表明,对学术转折点的搜索可以缩小到可视化网络中视觉上显著的节点。该方法提供了一种很有前景的方式,可将原本认知要求较高的任务简化为对地标、枢纽和中心的搜索。