Journal of Data and Information Science ›› 2019, Vol. 4 ›› Issue (2): 79-92.doi: 10.2478/jdis-2019-0010

• Research Paper • Previous Articles    

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

Zhesi Shen1,Fuyou Chen1,Liying Yang1,Jinshan Wu2†()   

  1. 1National Science Library, Chinese Academy of Sciences, Beijing 100190, P.R.China
    2School of Systems Science, Beijing Normal University, Beijing, 100875, P.R.China
  • Received:2019-04-03 Revised:2019-04-26 Online:2019-05-29 Published:2019-05-30


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.

Key words: Science mapping, Diversity, Graph embedding, Vector norm