2003
DOI: 10.1002/aris.1440370106
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Visualizing knowledge domains

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Cited by 1,172 publications
(762 citation statements)
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“…In processing and depicting the scientific structure of great domains, we further developed a methodology that follows the flow of knowledge domains and their mapping as proposed by Börner, Chen, and Boyack (2003).…”
Section: Methodsmentioning
confidence: 99%
“…In processing and depicting the scientific structure of great domains, we further developed a methodology that follows the flow of knowledge domains and their mapping as proposed by Börner, Chen, and Boyack (2003).…”
Section: Methodsmentioning
confidence: 99%
“…He can be considered the ISI´s top specialist in the research and development of science maps. [18][19][20][21][22][23][24][25] After the 90's, new methods of information retrieval and new techniques for the analysis, visualization and spatial positioning of information (well reviewed by Börner, Chen and Boyack 26 ), studies based on techniques for visualizing the structure of small scientific domains begin to proliferate. So, for instance, Braam, Moed and van Raan 27,28 propose the combined use of cocitation with co-word analysis for the generation of science maps, emphasizing their structure and dynamic aspects.…”
mentioning
confidence: 99%
“…Global approaches, best characterized by Shneiderman's mantra "overview, zoom & filter, details-on-demand" pattern in visual information seeking [42], have conventionally received much attention and have worked well for numerous kinds of data in many domains [43], [44], [45], [46], [47], [48], [49], [50], [42]. However, in this big data era, top-down approaches that focus on providing overviews of global information landscapes face significant challenges when applied to graphs with millions or billions of nodes and edges [49], [50]: graph overviews for large graphs are time-consuming to generate [8], [7]; the seminal work on graph clustering by Leskovec & Faloutsos [9] suggests there are simply no perfect overviews (i.e., no single best way to partition graphs into smaller communities), a view echoed by sensemaking literature in that people may have very different mental representations of information depending on their individual goals and prior experiences [51].…”
Section: A Graph Sensemakingmentioning
confidence: 99%