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.
| Original language | English |
|---|---|
| Pages (from-to) | 5303-5310 |
| Number of pages | 8 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Volume | 101 |
| Issue number | SUPPL. 1 |
| DOIs | |
| Publication status | Published - 2004 Apr 6 |
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All Science Journal Classification (ASJC) codes
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Searching for intellectual turning points : Progressive knowledge domain visualization. / Chen, Chaomei.
In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 101, No. SUPPL. 1, 06.04.2004, p. 5303-5310.Research output: Contribution to journal › Article
TY - JOUR
T1 - Searching for intellectual turning points
T2 - Progressive knowledge domain visualization
AU - Chen, Chaomei
PY - 2004/4/6
Y1 - 2004/4/6
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=1842687957&partnerID=8YFLogxK
U2 - 10.1073/pnas.0307513100
DO - 10.1073/pnas.0307513100
M3 - Article
C2 - 14724295
AN - SCOPUS:1842687957
VL - 101
SP - 5303
EP - 5310
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
SN - 0027-8424
IS - SUPPL. 1
ER -