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A hypergraph model for representing scientific output

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Abstract

Representation and analysis of publication data in the form of a network has become a common method of illustrating and evaluating the scientific output of a group or of a scientific field. Co-authorship networks also reveal patterns and collaboration practices. In this paper we propose the use of a hypergraph model—a generalized network—to represent publication data by considering papers as hypergraph nodes. Hyperedges, connecting the nodes, represent the authors connecting all their papers. We show that this representation is more straightforward than other authorship network models. Using the hypergraph model we propose a collaboration measure of an author that reflects the influence of that author over the collaborations of its co-authors. We illustrate the introduced concepts by analyzing publishing data of computer scientists and mathematicians in Romania over a 10 year period.

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Notes

  1. www.scopus.com accessed November 2016.

  2. To verify the nature of the distribution the software associated with Clauset et al. (2009) was used (Available at http://tuvalu.santafe.edu/aaronc/powerlaws/ Accessed November 2016).

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Acknowledgements

The authors would like to acknowledge the support received within the OPEN-RES Academic Writing Project 212/2012.

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Authors and Affiliations

  1. Center for the Study of Complexity, Babeş-Bolyai University, Cluj-Napoca, Romania

    Rodica Ioana Lung, Noémi Gaskó & Mihai Alexandru Suciu

Authors
  1. Rodica Ioana Lung
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  2. Noémi Gaskó
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  3. Mihai Alexandru Suciu
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Corresponding author

Correspondence to Noémi Gaskó.

Appendices

Appendix 1: Network measures

See Table 1 .

Table 1 Network indices for the constructed networks
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Appendix 2: Results of the power law test for the number of co-authors, papers published and collaboration measure

See Figs. 14, 15, 16, 17, 18 , 19.

Fig. 10
figure 10

The collaboration measure for each field and each year. Boxplots with large number of outliers indicate that there are few authors with high collaboration measures

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Fig. 11
figure 11

Mathematics (top) and Computer Science (bottom), aggregated network; histograms of collaboration measures and corresponding power law test. A p value lower than 0.0001 indicates that the null hypothesis that the data follows a power law distribution can be rejected (for both networks)

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Fig. 12
figure 12

Correlation matrices for the number of papers, number of co-authors, and correlation measure, respectively, for the field of Computer Science (top) and Mathematics (bottom). The main diagonal presents the histogram of the corresponding variable. A regression line and correlation value is presented for each pair of variables

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Fig. 13
figure 13

Correlation matrices for closeness centrality, betweenness centrality and collaboration measure \(\mu _C\) for each author, respectively, for the field of Computer Science (top) and Mathematics (bottom). The main diagonal presents the histogram of the corresponding variable. A regression line and correlation value is presented for each pair of variables

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Fig. 14
figure 14

Mathematics: the number of articles written by an author does not follow a power law distribution in years 2005, 2006, 2010, 2011, 2013, and 2014

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Fig. 15
figure 15

Computer Science: the number of articles written by an author does not follow a power law distribution in years 2005, 2006, 2009, 2010, and 2011

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Fig. 16
figure 16

Mathematics: the number of co-authors does not follow a power law distribution in years 2005, 2008, 2009, 2010, and 2012

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Fig. 17
figure 17

Computer Science: the number of co-authors does not follow a power law distribution in years 2005, 2006, 2007, 2010, and 2012

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Fig. 18
figure 18

Mathematics: collaboration measure does not follow a power law distribution in years 2005, 2006, 2008, 2009, 2010, and 2014

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Fig. 19
figure 19

Computer Science: the collaboration measure does not follow a power law distribution in years 2005, 2006, and 2007

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Lung, R.I., Gaskó, N. & Suciu, M.A. A hypergraph model for representing scientific output. Scientometrics 117, 1361–1379 (2018). https://doi.org/10.1007/s11192-018-2908-2

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