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Visualization and analysis of SCImago Journal & Country Rank structure via journal clustering

Antonio J. Gómez-Núñez (CSIC, SCImago Research Group Associated Unit, Granada, Spain)
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)
Félix Moya-Anegón (CSIC, SCImago Research Group Associated Unit, Granada, Spain)

Aslib Journal of Information Management

ISSN: 2050-3806

Article publication date: 19 September 2016




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.


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.


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.


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.



Gómez-Núñez, A.J., Vargas-Quesada, B., Chinchilla-Rodríguez, Z., Batagelj, V. and Moya-Anegón, F. (2016), "Visualization and analysis of SCImago Journal & Country Rank structure via journal clustering", Aslib Journal of Information Management, Vol. 68 No. 5, pp. 607-627.



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