Abstract
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.
Keywords
VisualizationData scienceBetweenness centralityComputer scienceCitationDomain (mathematical analysis)Information visualizationCentralityData miningWorld Wide Web
Affiliated Institutions
Related Publications
A Set of Measures of Centrality Based on Betweenness
A family of new measures of point and graph centrality based on early intuitions of Bavelas (1948) is introduced. These measures define centrality in terms of the degree to whic...
Using ggtree to Visualize Data on Tree‐Like Structures
Abstract Ggtree is an R/Bioconductor package for visualizing tree‐like structures and associated data. After 5 years of continual development, ggtree has been evolved as a packa...
An improved index of centrality
Increasing interest in the application of graph theory to the behavioral sciences is evidenced by the 1963 publication of the book "Applications of Graph Theory to Group Structu...
A Metric and an Ordering on Sets
Beginning with sets of arbitrary elements, concepts of distance and betweenness of sets are defined. Since betweenness as defined is not transitive, an investigation is made of ...
Bridge Centrality: A Network Approach to Understanding Comorbidity
Recently, researchers in clinical psychology have endeavored to create network models of the relationships between symptoms, both within and across mental disorders. Symptoms th...
Publication Info
- Year
- 2005
- Type
- article
- Volume
- 57
- Issue
- 3
- Pages
- 359-377
- Citations
- 5572
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
5572
OpenAlex
127
Influential
5045
CrossRef
Cite This
Chaomei Chen
(2005).
CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature.
Journal of the American Society for Information Science and Technology
, 57
(3)
, 359-377.
https://doi.org/10.1002/asi.20317
Identifiers
- DOI
- 10.1002/asi.20317