Node2vec Representation for Clustering Journals and as A Possible Measure of Diversity
Article Category: Research Paper
Published Online: Jun 07, 2019
Page range: 79 - 92
Received: Apr 03, 2019
Accepted: May 09, 2019
DOI: https://doi.org/10.2478/jdis-2019-0010
Keywords
© 2019 Zhesi Shen, Fuyou Chen, Liying Yang, Jinshan Wu, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
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.