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Journal of Data and Information Science  2019, Vol. 4 Issue (4): 1-12    DOI: 10.2478/jdis-2019-0017
Infrastructure of Scientometrics:The Big and Network Picture
Jinshan Wu()
School of Systems Science, Beijing Normal University, Beijing 100875, China
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A network is a set of nodes connected via edges, with possibly directions and weights on the edges. Sometimes, in a multi-layer network, the nodes can also be heterogeneous. In this perspective, based on previous studies, we argue that networks can be regarded as the infrastructure of scientometrics in the sense that networks can be used to represent scientometric data. Then the task of answering various scientometric questions related to this data becomes an algorithmic problem in the corresponding network.

Key wordsNetwork science      Scientometrics     
Received: 16 November 2019      Published: 11 December 2019
Corresponding Authors: Jinshan Wu     E-mail:
Cite this article:

Jinshan Wu. Infrastructure of Scientometrics:The Big and Network Picture. Journal of Data and Information Science, 2019, 4(4): 1-12.

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Figure 1. A three-layer network of scientometric relational data: Authors, papers and concepts on the one hand; and inventors, patents, and technology concepts on the other.
[1]   Alvarez-Hamelin J.I., Dall’Asta L., Barrat A., & Vespignani A. (2008). K-core decomposition of Internet graphs: Hierarchies, selfsimilarity and measurement biases. Networks and Heterogeneous Media, 3(2), 371-393. doi:10.3934/nhm.2008.3.371. Annual European Conference on Complex Systems, Dresden, GERMANY, OCT 01-06, 2007.
doi: 10.3934/nhm
[2]   Amjad T., Ding Y., Daud A., Xu J., & Malic V. (2015). Topic-based heterogeneous rank.Scientometrics, 104(1), 313-334. doi:10.1007/s11192-015-1601-y.
[3]   Brin S., &Page ,L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30, 107-117. In proceedings of the Seventh International World Wide Web Conference. doi:10.1016/S0169-7552(98)00110-X.
[4]   Chen ,C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377. doi:10.1002/asi.20317.
doi: 10.1002/(ISSN)1532-2890
[5]   Gy?ngyi Z., Garcia-Molina H., & Pedersen J. (2004). Combating web spam with trustrank.In proceedings of the Thirtieth international conference on Very large data bases, 30, 576-587.
[6]   Leontief , W. (1941). The Structure of American Economy, 1919-1929. Cambridge: HarvardUniversity Press.
[7]   Mingers ,J., &Leydesdorff ,L. (2015). A review of theory and practice in scientometrics. Europen Journal of Operational Research, 246(1), 1-19. doi:10.1016/j.ejor.2015.04.002.
doi: 10.1016/j.ejor.2015.04.030 pmid: 26435573
[8]   Otte ,E.,& Rousseau ,R. (2002). Social network analysis: A powerful strategy, also for the information sciences. Journal of Information Science, 28(6), 441-453. doi:10.1177/016555102762202123.
doi: 10.5888/pcd13.160013 pmid: 27253636
[9]   Pinski ,G., &Narin ,F. (1976). Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics. Information Processing & Management, 12(5), 297-312. doi:10.1016/0306-4573(76)90048-0.
doi: 10.1016/j.compmedimag.2019.101685 pmid: 31846826
[10]   Rousseau R., Egghe L., & Guns, R. (2018). Becoming Metric-Wise.Chandos Information Professional Series.Chandos Publishing.doi:10.1016/B978-0-08-102474-4.00010-8.
[11]   Shen Z., Chen F., Yang L., & Wu J. (2019). Node2vec representation for clustering journals and as a possible measure of diversity. Journal of Data and Information Science, 4(2), 79-92. doi:10.2478/jdis-2019-0010.
doi: 10.2478/jdis-2019-0010
[12]   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(1), 82-97. doi:10.1016/j.joi.2015.11.002.
doi: 10.1002/1097-4679(195401)10:1&<82::aid-jclp2270100119>;2-h pmid: 13117989
[13]   Waltman , L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2), 365-391. doi:10.1016/j.joi.2016.02.007.
doi: 10.1097/TA.0000000000002532 pmid: 31688786
[14]   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(12), 2378-2392. doi:10.1002/asi.22748.
doi: 10.1002/asi.v63.12
[15]   West J.D., Bergstrom T.C., & Bergstrom C.T. (2010). The Eigenfactor Metrics (TM): A network approach to assessing scholarly journals. College & Research Libraries, 71(3), 236-244. doi:10.5860/0710236.
doi: 10.1111/hsc.12920 pmid: 31847057
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