Optimizing SCImago Journal & Country Rank classification by community detection

• Published in: Journal of informetrics Vol. 8; no. 2; pp. 369 - 383
• Main Authors: Gómez-Núñez, Antonio J., Batagelj, Vladimir, Vargas-Quesada, Benjamín, Moya-Anegón, Félix, Chinchilla-Rodríguez, Zaida
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.04.2014; Elsevier Science Ltd
• Subjects: Algorithms; Bibliometrics; Citation-based network; Clustering; Community detection; Journal classification; SCImago Journal & Country Rank; Subject indexing; Community detection; Journal classification; Citation-based network; SCImago Journal & Country Rank; Clustering
• ISSN: 1751-1577; 1875-5879
• DOI: 10.1016/j.joi.2014.01.011

•The combination of citation-based networks succeeds in optimizing journal classification.•VOS and Louvain clustering algorithms work fine on large journal citation networks.•VOS and Louvain clustering solutions enhanced original SJR journal classification.•Both algorithms showed a very similar performance in journal classification. Subject classification arises as an important topic for bibliometrics and scientometrics, searching to develop reliable and consistent tools and outputs. Such objectives also call for a well delimited underlying subject classification scheme that adequately reflects scientific fields. Within the broad ensemble of classification techniques, clustering analysis is one of the most successful. Two clustering algorithms based on modularity – the VOS and Louvain methods – are presented here for the purpose of updating and optimizing the journal classification of the SCImago Journal & Country Rank (SJR) platform. We used network analysis and Pajek visualization software to run both algorithms on a network of more than 18,000 SJR journals combining three citation-based measures of direct citation, co-citation and bibliographic coupling. The set of clusters obtained was termed through category labels assigned to SJR journals and significant words from journal titles. Despite the fact that both algorithms exhibited slight differences in performance, the results show a similar behaviour in grouping journals. Consequently, they are deemed to be appropriate solutions for classification purposes. The two newly generated algorithm-based classifications were compared to other bibliometric classification systems, including the original SJR and WoS Subject Categories, in order to validate their consistency, adequacy and accuracy. In addition to some noteworthy differences, we found a certain coherence and homogeneity among the four classification systems analysed.