Abstract
Abstract Purpose To investigate the effectiveness of using node2vec on journal citation networks to represent journals as vectors for tasks such as clustering, science mapping, and journal diversity measure. Design/methodology/approach Node2vec is used in a journal citation network to generate journal vector representations. Findings 1. Journals are clustered based on the node2vec trained vectors to form a science map. 2. The norm of the vector can be seen as an indicator of the diversity of journals. 3. Using node2vec trained journal vectors to determine the Rao-Stirling diversity measure leads to a better measure of diversity than that of direct citation vectors. Research limitations All analyses use citation data and only focus on the journal level. Practical implications Node2vec trained journal vectors embed rich information about journals, can be used to form a science map and may generate better values of journal diversity measures. Originality/value The effectiveness of node2vec in scientometric analysis is tested. Possible indicators for journal diversity measure are presented.
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