Scientific research is highly dynamic. New areas of science continually evolve; others gain or lose importance, merge or split. Due to the steady increase in the number of scientific publications it is hard to keep an overview of the structure and dynamic development of one's own field of science, much less all scientific domains. However, knowledge of 'hot' topics, emergent research frontiers, or change of focus in certain areas is a critical component of resource allocation decisions in research labs, governmental institutions, and corporations. This paper demonstrates the utilization of Kleinberg's burst detection algorithm, co-word occurrence analysis, and graph layout techniques to generate maps that support the identification of major research topics and trends. The approach was applied to analyze and map the complete set of papers published in the Proceedings of the National Academy of Sciences (PNAS) in the years 1982-2001. Six domain experts examined and commented on the resulting maps in an attempt to reconstruct the evolution of major research areas covered by PNAS. Maps depicting the structure and evolution of scientific fields are also called Knowledge Domain Visualizations (KDVs) [2] . They are a special kind of Information Visualization [3] that exploit powerful human vision and spatial cognition to help humans mentally organize and electronically access and manage large, complex information spaces. Unlike scientific visualizations, KDVs are created from data that have no spatial reference, such as sets of publications, patents, or grants. KDVs use sophisticated data analysis and visualization techniques to objectively identify major research areas, experts, institutions, grants, publications, journals, etc., in a domain of interest. They can be used to gain an overview of a knowledge domain; its homogeneity, import-export factors, and relative speed; to track the emergence and evolution of topics; or to help identify the most productive as well as new research areas. Benefits of KDVs include reducing visual search time, revealing hidden relations, displaying data sets from several perspectives simultaneously, facilitating hypothesis formulation, serving as effective means of communication, and prompting users to think in new ways about document data. Today, KDVs are typically generated semi-automatically from rather small, static data sets, and for a specific knowledge domain and information need. This paper presents a novel way to generate co-word association maps of major topics based on highly frequent words and words with a sudden increase in their usage, a phenomenon called 'burst' [4] . A large-scale data set comprising the complete set of 47,073 papers published in the Proceedings of the National Academy of Sciences (PNAS) in the years 1982-2001 is used for demonstration. We describe the identification of highly frequent and bursty words, the analysis of the most important correlations among those words, and the generation and interpretation of a two-dimensional layout showing major research topics and their dynamics.
Mane and Börner REF visualize 50 frequent and bursty words in their analysis of publication of the Proceeding of the National Academy of Sciences.
Mapping Topics and Topic Bursts in PNAS
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Computer Science Medicine