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

Shen, Zhesi; Chen, Fuyou; Yang, Liying; Wu, Jinshan

doi:10.2478/jdis-2019-0010

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.

References

Boyack, K., Glänzel, W., Gläser, J., Havemann, F., Scharnhorst, A., Thijs, B., van Eck, N. J., Velden, T., & Waltmann, L. (2017). Topic identification challenge. Scientometrics, 111, 1223– 1224.
Search in Google Scholar Back to article
Boyack, K. W., & Klavans, R. (2014). Including cited non-source items in a large-scale map of science: What difference does it make? Journal of Informetrics, 8, 569–580. 10.1016/j. joi.2014.04.001
Open DOI Search in Google Scholar Back to article
Colavizza, G., Boyack, K. W., van Eck, N. J., & Waltman, L. (2018). The closer the better: Similarity of publication pairs at different cocitation levels. Journal of the Association for Information Science and Technology, 69, 600–609. 10.1002/asi.23981
Open DOI Search in Google Scholar Back to article
Glänzel, W., & Schubert, A. (2003). A new classification scheme of science fields and subfields designed for scientometric evaluation purposes. Scientometrics, 56, 357–367.
Search in Google Scholar Back to article
Glänzel, W., & Thijs, B. (2011). Using core documents for the representation of clusters and topics. Scientometrics, 88, 297–309.
Search in Google Scholar Back to article
Grover, A., & Leskovec, J. (2016). node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 855–864). ACM.
Search in Google Scholar Back to article
Haunschild, R., Schier, H., Marx, W., & Bornman, L. (2018). Algorithmically generated subject categories based on citation relations: An empirical micro study using papers on overall water splitting. Journal of Informetrics, 12, 436–447. 10.1016/j.joi.2018.03.004
Open DOI Search in Google Scholar Back to article
Janssens, F., Glänzel, W., & De Moor, B. (2008). A hybrid mapping of information science. Scientometrics, 75, 607–631.
Search in Google Scholar Back to article
JCR2017 (2018). 2017 journal impact factor, journal citation reports (clarivate analytics, 2018).
Search in Google Scholar Back to article
Klavans, R., & Boyack, K. W. (2009). Toward a consensus map of science. Journal of the American Society for Information Science and Technology, 60, 455–476.
Search in Google Scholar Back to article
Klavans, R., & Boyack, K. W. (2017). Which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge? Journal of the Association for Information Science and Technology, 68, 984–998.
Search in Google Scholar Back to article
Leydesdorff, L. (2006). Can scientific journals be classified in terms of aggregated journal-journal citation relations using the journal citation reports? Journal of the American Society for Information Science and Technology, 57, 601–613.
Search in Google Scholar Back to article
Leydesdorff, L., Bornmann, L., & Wagner, C. S. (2017). Generating clustered journal maps: An automated system for hierarchical classification. Scientometrics, 110, 1601–1614.
Search in Google Scholar Back to article
Leydesdorff, L., Bornmann, L., & Wagner, C. S. (2017). Generating clustered journal maps: an automated system for hierarchical classification. Scientometrics, 110, 1601–1614. 10.1007/s11192-016-2226-5
Open DOI Search in Google Scholar Back to article
Leydesdorff, L., Wagner, C. S., & Bornmann, L. (2018). Betweenness and diversity in journal citation networks as measures of interdisciplinarityâ€”a tribute to eugene garfield. Scientometrics, 114, 567–592.
Search in Google Scholar Back to article
Maaten, L. v. d., & Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9, 2579–2605.
Search in Google Scholar Back to article
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In advances in neural information processing systems (pp. 3111–3119).
Search in Google Scholar Back to article
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Search in Google Scholar Back to article
Rao, C. R. (1982). Diversity: its measurement, decomposition apportionment and analysis. Sankhy : The Indian Journal of Statistics, Series A, 44, 1–22.
Search in Google Scholar Back to article
Schakel, A. M., & Wilson, B. J. (2015). Measuring word significance using distributed representations of words. arXiv preprint arXiv:1508.02297.
Search in Google Scholar Back to article
Shen, Z., Yang, L., Pei, J., Li, M., Wu, C., Bao, J., Wei, T., Di, Z., Rousseau, R., & Wu, J. (2016). Interrelations among scientific fields and their relative influences revealed by an input—output analysis. Journal of Informetrics, 10, 82–97. https://doi.org/10.1016/j.joi.2015.11.002
Search in Google Scholar Back to article
Sjogarde, P., & Ahlgren, P. (2018). Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics. Journal of Informetrics, 12, 133–152. 10.1016/j.joi.2017.12.006
Open DOI Search in Google Scholar Back to article
Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface, 4, 707–719.
Search in Google Scholar Back to article
Vinh, N. X., Epps, J., & Bailey, J. (2010). Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. Journal of Machine Learning Research, 11, 2837–2854.
Search in Google Scholar Back to article
Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10, 365 – 391. https://doi.org/10.1016/j.joi.2016.02.007
Search in Google Scholar Back to article
Waltman, L., & Van Eck, N. J. (2012). A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology, 63, 2378–2392.
Search in Google Scholar Back to article
Zhang, L., Rousseau, R., & Glanzel, W. (2016). Diversity of references as an indicator of the interdisciplinarity of journals: Taking similarity between subject fields into account. Journal of The Association for Information Science and Technology, 67, 1257–1265. 10.1002/asi.23487
Open DOI Search in Google Scholar Back to article