Visualization and analysis of SCImago Journal & Country Rank structure via journal clustering
| Pages | 607-627 |
| DOI | https://doi.org/10.1108/AJIM-12-2015-0205 |
| Date | 19 September 2016 |
| Published date | 19 September 2016 |
| Author | Antonio J. Gómez-Núñez,Benjamin Vargas-Quesada,Zaida Chinchilla-Rodríguez,Vladimir Batagelj,Félix Moya-Anegón |
| Subject Matter | Library & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management |
Visualization and analysis
of SCImago Journal &
Country Rank structure
via journal clustering
Antonio J. Gómez-Núñez and Benjamin Vargas-Quesada
CSIC, SCImago Research Group Associated Unit, Granada, Spain
Zaida Chinchilla-Rodríguez
Consejo Superior de Investigaciones Científicas, Madrid, Spain
Vladimir Batagelj
University of Ljubljana, Ljubljana, Slovenia, and
Félix Moya-Anegón
CSIC, SCImago Research Group Associated Unit, Granada, Spain
Abstract
Purpose –The purpose of this paper is to visualize the structure of SCImago Journal & Country Rank
(SJR) coverage of the extensive citation network of Scopus journals, examining this bibliometric portal
through an alternative approach, applying clustering and visualization techniques to a combination of
citation-based links.
Design/methodology/approach –Three SJR journal-journal networks containing direct citation,
co-citation and bibliographic coupling links are built. The three networks were then combined into a
new one by summing up their values, which were later normalized through geo-normalization measure.
Finally, the VOS clustering algorithm was executed and the journal clusters obtained were labeled
using original SJR category tags and significant words from journal titles.
Findings –The resultant scientogram displays the SJR structure through a set of communities
equivalent to SJR categories that represent the subject contents of the journals they cover. A higher
level of aggregation by areas provides a broad view of the SJR structure, facilitating its analysis and
visualization at the same time.
Originality/value –This is the first study using Persson’s combination of most popular citation-
based links (direct citation, co-citation and bibliographic coupling) in order to develop a scientogram
based on Scopus journals from SJR. The integration of the three measures along with performance of
the VOS community detection algorithm gave a balanced set of clusters. The resulting scientogram is
useful for assessing and validating previous classifications as well as for information retrieval and
domain analysis.
Keywords Classification, Clustering, Information Visualization, Citation-based links, Scientograms,
SCImago Journal & Country Rank
Paper type Research paper
Introduction
Information visualization has emerged as a discipline of great interest at the cross-
roads of bibliometrics and scientometrics, providing multiple visual representations
known as scientograms or science maps (Moya-Anegón et al., 2007). They can facilitate,
for instance, the analysis of a scientific domain by depicting the structure of research
output through a set of subject disciplines along with their relationships and
interactions (Vargas-Quesada and Moya-Anegón, 2007). Generally, these maps are
derived from the scientific literature included in academic databases by defining a unit
Aslib Journal of Information
Management
Vol. 68 No. 5, 2016
pp. 607-627
©Emerald Group Publis hing Limited
2050-3806
DOI 10.1108/AJIM-12-2015-0205
Received 19 December 2015
Revised 7 July 2016
Accepted 18 July 2016
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2050-3806.htm
607
SCImago
Journal &
Country Rank
structure
of analysis, such as papers, journals or categories, and a unit of measure based on
citation links (direct citation, co-citation, coupling), the text (title, abstract, addresses) or
a combination of both. Apart from showing the disciplinary structure of science and
research, scientograms enable one to explore the sequential evolution of research,
identify research fronts, detect emerging or decadent topics, and find areas of
interdisciplinary efforts.
The Web of Science (WoS) (Thomson Reuters, 2009) and Scopus (Elsevier, 2004) are
currently held to be the top databases for academic and scientific information by the
majority of research community, given their extensive coverage over disciplines and
time. In addition to supplying detailed bibliographic information from a vast number of
prestigious peer-reviewed journals from all over the world, both databases have
citation indices that serve to develop numerous bibliometric indicators. These
indicators, which can be qualitative or quantitative, are of great value in evaluating
science and research, particularly for decision and policy makers. However, in
developing and designing indicators and tools relying on scientific literature included
in databases, a correct classification of publications is essential for arriving at
consistent and reliable results.
Generating scientograms calls for the association and distribution of the items to be
represented, which are mapped according to their influence, similarity or interactions
with others. The degree of relatedness may be calculated in several ways, for instance,
considering the co-occurrence of significant words from parts of the text (title, abstract,
keywords, etc.) or the number of shared references. Through statistical techniques such
as clustering or factor analysis one can uncover interrelated subject groups, thereby
perceiving a breakdown of scientific knowledge into different disciplines. The array of
software for network visualization and analysis includes Pajek (Batagelj and
Mrvar, 1997), Gephi (Bastian et al., 2009), Sci2 Tool (Sci2 Team, 2009) and VOSViewer
(Van Eck and Waltman, 2010), featuring different clustering algorithms that
decompose the network into several groups of strongly interrelated or similar items
(sub-networks). Thus, visualization software is an effective solution for the refinement
of literature classification in databases as well.
Clustering and information visualization
Among the diverse statistical and bibliometric techniques used for classification and
visualization analysis we have factor analysis (Leydesdorff, 2006; Vargas-Quesada
et al., 2008), reference analysis (Glänzel and Schubert, 2003; Archambault et al., 2011;
Gómez-Núñez et al., 2011) and clustering. The latter has become very popular in studies
of subject groups within citation or text networks. In the field of information
visualization, clustering methods have been frequently used by researchers to delinea te
the structure of knowledge and research. The classification scheme of different
disciplines and/or sub-disciplines of scientific knowledge must be consistent and
effective. As stated by Boyack and Klavans (2014) “science mapping, when reduced to
its most basic components, is a combination of classification and visualization.”Some
significant proposals involving clustering to build maps of science based on WoS and
Scopus (Klavans and Boyack, 2009) aim to develop a consensual map of science derived
from previously examined maps.
Many clustering experiments have been conducted on different levels of aggregation.
At the document level, Small (1999a, b) developed a hierarchical map of science through a
method that combined fractional counting of cited documents, single- and complete-linkage
clustering and two-dimensional ordination based on a geometric triangulation process.
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