The rationale is to highlight articles that increased most in the early years of publication. In the following superstring example, we use a simple model to normalize the citations of an article within each time slice by the logarithm of its publication age, the number of years elapsed since its publication year. To detect intellectual turning points, we are particularly interested in articles that have rapidly growing citations. For example, success breeds success a highly cited article is likely to receive more citations than a currently less frequently cited article. Citations depend on many underlying factors. Some articles have much more than their fair share of citations, some have less, and some have none at all. In the citation world, articles are not created equal. We expect that the progressive method described in this article can provide a useful instrument for examining the evolution of a scientific network, and that the concrete example of network evolution can lead to insights into a broader range of networks. We emphasize the integral role of the semantics of such networks in understanding the profound dynamics of network evolution. However, much of the work has concentrated on abstract network representations rather than on concrete networks and their practical implications. Various growth models such as preferential attachment ( 10- 12) have been developed in the study of network evolution. Studies in complex network analysis, especially in relation to small-world and scale-free networks, focus on two broad issues, namely topological properties and generative mechanisms of networks. The recent interest in complex network analysis is a potentially fruitful route to improve our understanding of scientific networks as well as general networks ( 10).
The method provides a promising way to simplify otherwise cognitively demanding tasks to a search for landmarks, pivots, and hubs.
The analysis has demonstrated that a search for intellectual turning points can be narrowed down to visually salient nodes in the visualized network. 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 study focuses on the search of articles that triggered two superstring revolutions. The method is applied to a cocitation study of the superstring field in theoretical physics. 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.
DREXEL PHYSICS 101 Q SERIES
The method first derives a sequence of cocitation networks from a series of equal-length time interval slices. This article introduces a previously undescribed method progressively visualizing the evolution of a knowledge domain's cocitation network.