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.

中文翻译：

---

Node2vec表示用于聚类期刊，并作为多样性的一种可能度量

摘要目的研究在期刊引文网络上使用node2vec将期刊表示为矢量的有效性，以完成诸如聚类，科学制图和期刊多样性测度等任务。设计/方法/方法Node2vec在期刊引文网络中用于生成期刊矢量表示。结果1.基于node2vec训练过的向量将期刊聚在一起，形成一张科学图。2.向量的范数可以看作是期刊多样性的指标。3.使用node2vec训练的日志向量确定Rao-Stirling分集度量比直接引用向量可以更好地度量分集。研究局限性所有分析都使用引文数据，并且仅关注期刊级别。实际意义Node2vec训练有素的期刊向量嵌入了有关期刊的丰富信息，可以用来形成科学地图，并可以产生更好的期刊多样性度量值。独创性/价值测试了node2vec在科学计量分析中的有效性。介绍了期刊多样性测度的可能指标。