基于可视化的作者同被引技术的发展

自 198 1年作者同被引技术 (ACA)开发以来 ,作为一种揭示科学结构行之有效的方法 ,被学术界认可并广为采用 ,但烦琐的程序、计算中存在的问题严重阻碍了其发展。信息可视化浪潮为ACA的发展带来了契机。本文重点阐述了将信息可视化技术用于作者同被引分析的成果。     

Binary Pathfinder: An improvement to the Pathfinder algorithm

The Pathfinder algorithm is widely used to prune social networks. The pruning maintains the geodesic distances between nodes. It has shown itself to be very useful in the analysis of, amongst others, citations in BIS (bibliometrics, informetrics, and scientometrics). It has even been proposed for the online display of the search results in an information retrieval system. However, its great time and space complexity limits its use in real-time applications and in networks of any considerable size.The present work describes an improved algorithm with considerably reduced time and space complexity. Its lower execution costs thus increase its applicability both in real time and to large networks.     

A new variant of the Pathfinder algorithm to generate large visual science maps in cubic time

In the last few years, there is an increasing interest to generate visual representations of very large scientific domains. A methodology based on the combined use of ISI–JCR category cocitation and social networks analysis through the use of the Pathfinder algorithm has demonstrated its ability to achieve high quality, schematic visualizations for these kinds of domains. Now, the next step would be to generate these scientograms in an on-line fashion. To do so, there is a need to significantly decrease the run time of the latter pruning technique when working with category cocitation matrices of a large dimension like the ones handled in these large domains (Pathfinder has a time complexity order of O(n⁴), with n being the number of categories in the cocitation matrix, i.e., the number of nodes in the network).