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Fig. 8.18
Areas of research leadership for China.
Left
: A discipline-level circle map.
Right
:
A paper-level circle map embedded in a discipline circle map. Areas of research leadership are
located at the average position of corresponding disciplines or paradigms. The intensity of the
nodes indicates the number of leadership types found, Relative Publication Share (
RPS
), Relative
Reference Share (
RRS
), or state-of-the art (
SOA
) (Reprinted from Klavans and Boyack
2010
with
permission)
their dissimilarity; the other is to group individual journals into clusters based on
the distance generated by the layout process.
The map layout was made using the VxOrd algorithm, which ignores long-range
links in its layout process. The proximity of nodes in the resultant graph layout
was used to identify clusters using a modified single-linkage clustering algorithm.
In single linkage, the distance between two clusters is computed as the distance
between the two closest elements in the two clusters. The resultant map contains
812 clusters of journals and conference proceedings (See Fig.
8.19
). The map was
used as a base map for a variety of overlays. In particular, the presence of an
institution can be depicted with this map. A cluster with a clear circle contains
journal papers only. In contrast, a cluster with a shaded circle contains proceeding
papers. As shown in the map, the majority of proceeding papers are located between
computer science (CS) and Physics. Disciplines such as Virology are almost entirely
dominated by journal papers.
More recently, Klavans and Boyack created a new global map of science based on
Scopus 2010. The new Scopus 2010 map is a paper-level map, representing 116,000
clusters of 1.7 million papers (See Fig.
8.20
). The Scopus 2010 map is hybrid in
that clusters were generated from citations and the layout was done based on text
similarity. The similarities between clusters were calculated based on words from
titles and abstracts of papers in each cluster using the Okapi BM25 text similarity.
The clustering step did not use a hybrid similarity based on both text and citation
simultaneously. For each cluster, 5-15 clusters with the strongest connections were
retained. Labels of clusters were manually added.
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