Name
Title | ||
|---|---|---|
Node2vec Representation For Clustering Journals And As A Possible Measure Of Diversity |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.2478/jdis-2019-0010 | JOURNAL OF DATA AND INFORMATION SCIENCE |
Keywords | DocType | Volume |
Science mapping, Diversity, Graph embedding, Vector norm | Journal | 4 |
Issue | ISSN | Citations |
2 | 2096-157X | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhesi Shen | 1 | 1 | 1.70 |
| Fuyou Chen | 2 | 0 | 0.34 |
| Liying Yang | 3 | 11 | 7.05 |
| Jinshan Wu | 4 | 23 | 7.62 |