Node2vec Representation for Clustering Journals and as A Possible Measure of Diversity

Purpose: To investigate the effectiveness of using node2 vec on journal citation networks to represent journals as vectors for tasks such as clustering, science mapping, and journal diversity measure.Design/methodology/approach: Node2 vec is used in a journal citation network to generate journal vector representations. Findings: 1. Journals are clustered based on the node2 vec 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 node2 vec 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: Node2 vec 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 node2 vec in scientometric analysis is tested. Possible indicators for journal diversity measure are presented.