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Chen, C.: CiteSpace II : detecting and visualizing emerging trends and transient patterns in scientific literature (2006)
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- Abstract
- This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science: research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature - an evolving network of scientific publications cited by research-front concepts. Kleinberg's (2002) burst-detection algorithm is adapted to identify emergent research-front concepts. Freeman's (1979) betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are that (a) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, (b) the value of a co-citation cluster is explicitly interpreted in terms of research-front concepts, and (c) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981-2004) and terrorism (1990-2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.
- Date
- 22. 7.2006 16:11:05
-
Sidhom, S.; Hassoun, M.: Morpho-syntactic parsing to text mining environment : NP recognition model to knowledge visualization and information (2003)
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- Year
- 2003
-
Leide, J.E.; Large, A.; Beheshti, J.; Brooks, M.; Cole, C.: Visualization schemes for domain novices exploring a topic space : the navigation classification scheme (2003)
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- Source
- Information processing and management. 39(2003) no.6, S.923-940
- Year
- 2003
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Platis, N. et al.: Visualization of uncertainty in tag clouds (2016)
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- Date
- 1. 2.2016 18:25:22
-
Beagle, D.: Visualizing keyword distribution across multidisciplinary c-space (2003)
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- Source
- D-Lib magazine. 9(2003) no.6, x S
- Year
- 2003
-
Osinska, V.; Bala, P.: New methods for visualization and improvement of classification schemes : the case of computer science (2010)
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- Date
- 22. 7.2010 19:36:46
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Börner, K.; Chen, C.; Boyack, K.W.: Visualizing knowledge domains (2002)
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- Annual review of information science and technology. 37(2003), S.179-258
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Wu, K.-C.; Hsieh, T.-Y.: Affective choosing of clustering and categorization representations in e-book interfaces (2016)
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- Date
- 20. 1.2015 18:30:22
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Wu, I.-C.; Vakkari, P.: Effects of subject-oriented visualization tools on search by novices and intermediates (2018)
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- 9.12.2018 16:22:25
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Osinska, V.; Kowalska, M.; Osinski, Z.: ¬The role of visualization in the shaping and exploration of the individual information space : part 1 (2018)
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- Date
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Spero, S.: LCSH is to thesaurus as doorbell is to mammal : visualizing structural problems in the Library of Congress Subject Headings (2008)
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Batorowska, H.; Kaminska-Czubala, B.: Information retrieval support : visualisation of the information space of a document (2014)
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