• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.947-957
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• 2016

KCI is a database for the citations of journals and papers published in Korea. Classification of a journal listed in KCI was mainly determined by the publisher who registered the journal at the time of application for the journal. However, journal classification in KCI was known for not properly representing the quoting rate between journals. In this study, we extracted communities of the journals registerd in KCI based on quoting relationship using various network clustering algorithms. Among them, the infomap algorithm turned out to give a classification more being alike to the current KCI's in the aspect of the modular structure.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.827-839
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• 2015

Extraction of communities from a journal citation database by the citation structure is a useful tool to see closely related groups of the journals. SCI of Thomson Reuters or SCOPUS of Elsevier have had tried to grasp community structure of the journals in their indices according to citation relationships, but such a trial has not been made yet with the Korean Citation Index, KCI. Therefore, in this study, we extracted communities of the journals of the natural science area in KCI, using various clustering algorithms for a social network based on citations among the journals and compared the groups obtained with the classfication of KCI. The infomap algorithm, one of the clustering methods applied in this article, showed the best grouping result in the sense that groups obtained by it are closer to the KCI classification than by other algorithms considered and reflect well the citation structure of the journals. The classification results obtained in this study might be taken consideration when reclassification of the KCI journals will be made in the future.