title: Searching for intellectual turning points: progressive knowledge domain visualization.
abstract: 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.
authors.affiliation: College of Information Science and Technology, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104-2875, USA. chaomei.chen@cis.drexel.edu
authors.forename: Chaomei
authors.initials: C
authors.lastname: Chen
mesh_terms: D020407:Internet / Q000639:trends*; D019359:Knowledge; D012586:Science / Q000639:trends*; D012984:Software; D013997:Time Factors