Chen, C.: CiteSpace II : detecting and visualizing emerging trends and transient patterns in scientific literature (2006)
0.01
0.0144263785 = product of:
0.043279134 = sum of:
0.029076494 = weight(_text_:b in 5272) [ClassicSimilarity], result of:
0.029076494 = score(doc=5272,freq=2.0), product of:
0.14855953 = queryWeight, product of:
3.542962 = idf(docFreq=3476, maxDocs=44218)
0.041930884 = queryNorm
0.19572285 = fieldWeight in 5272, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.542962 = idf(docFreq=3476, maxDocs=44218)
0.0390625 = fieldNorm(doc=5272)
0.014202639 = product of:
0.028405279 = sum of:
0.028405279 = weight(_text_:22 in 5272) [ClassicSimilarity], result of:
0.028405279 = score(doc=5272,freq=2.0), product of:
0.1468348 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.041930884 = queryNorm
0.19345059 = fieldWeight in 5272, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=5272)
0.5 = coord(1/2)
0.33333334 = coord(2/6)
- 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
