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 timevariant
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) burstdetection
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
