以下内容来自： http://www.visualcomplexity.com/vc/index.cfm?domain=Knowledge%20Networks Mapping Topics and Topic Bursts in PNAS Author(s):Ketan K. Mane, Katy Borner Institution:Indiana University Year:2004 URL:http://www.pnas.org/content/vol101/suppl_1/ Project Description: This work demonstrates an approach to map the dynamics of scientific knowledge via analysis of the complete set of papers published in the Proceedings of the National Academy of Science (PNAS) in the years 1982-2001. Data analysis techniques are applied to citation data within this corpus to analyze and visualize highly frequent words as well as sudden increases in the usage patterns of words, called 'bursts'. Author(s):W. Bradford Paley Institution:Digital Image Design Incorporated Year:2002 URL:http://www.textarc.org/ Project Description: TextArc: Revealing Word Associations, Distribution and Frequency. TextArc is a tool designed to help people discover patterns and concepts in any text by leveraging a powerful, underused resource: human visual processing. It compliments approaches such as Statistical Natural Language Processing and Computational Linguistics by providing an overview, letting intuition help extract meaning from an unread text. Here, an analysis of Lewis Carroll's Alice in Wonderland demonstrates TextArc's structure and some capabilities. TextArc represents the entire text as two concentric spirals on the screen: each line is drawn in a tiny (one pixel tall) font around the outside, starting at the top; then each word is drawn in a more readable size. Important typographic features, like the mouse-tail shape of a poem at about two o'clock, can be seen because the tiny lines retain their formatting. Frequently used words stand out from the background more intensely. Mathematical Physics Archive Author(s):Jean-Pierre Eckmann Institution:(unknown) Year:2003 URL:http://mcdb.colorado.edu/courses/3280/lectures/class16-2.html Project Description: This project was originally produced by Jean-Pierre Eckmann for the IAMP Conference 2003 in Lisbon. The concept was later pursued by Sid Redner, from Boston University, who showed that the network of scientific papers, connected by citations, has a power law degree distribution. In a test of this concept papers from the Mathematical Physics Archive were examined for reciprocal citation of the authors, yielding the preceding graph, where colors indicate areas of obvious curvature that were then checked for content. The strongest curvature is the area in red, statistical mechanics, which is reasonable given the nature of the database. This result emerged from the analysis that did not use scientific field as a parameter in generating the clusters. Visualizing Knowledge Domains Author(s):Katy Borner, Chaomei Chen, Kevin Boyack Institution:School of Library and Information Science - Indiana University Year:2002 URL:http://tinyurl.com/ykurjf Project Description: An overview of the author co-citation map (1977-2001), consisting of 380 authors with 9 or more citations. The map is dominated by the largest specialty of citation indexing. No strong concentration of other specialties are found, which implies the diversity of the domain. This map is a landscape view of the ACA map. The height of a citation bar indicates the number of citations for the correspondent author. The spectrum of colors on each citation shows the time when citations were made. Authors with more than 50 citations are displayed with semi-transparent labels.
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