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Three-feature model to reproduce the topology of citation networks and the effects from authors’ visibility on their h-index

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  • Amancio, Diego Raphael
  • Oliveira, Osvaldo Novais
  • da Fontoura Costa, Luciano

Abstract

Various factors are believed to govern the selection of references in citation networks, but a precise, quantitative determination of their importance has remained elusive. In this paper, we show that three factors can account for the referencing pattern of citation networks for two topics, namely “graphenes” and “complex networks”, thus allowing one to reproduce the topological features of the networks built with papers being the nodes and the edges established by citations. The most relevant factor was content similarity, while the other two – in-degree (i.e. citation counts) and age of publication – had varying importance depending on the topic studied. This dependence indicates that additional factors could play a role. Indeed, by intuition one should expect the reputation (or visibility) of authors and/or institutions to affect the referencing pattern, and this is only indirectly considered via the in-degree that should correlate with such reputation. Because information on reputation is not readily available, we simulated its effect on artificial citation networks considering two communities with distinct fitness (visibility) parameters. One community was assumed to have twice the fitness value of the other, which amounts to a double probability for a paper being cited. While the h-index for authors in the community with larger fitness evolved with time with slightly higher values than for the control network (no fitness considered), a drastic effect was noted for the community with smaller fitness.

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  • Amancio, Diego Raphael & Oliveira, Osvaldo Novais & da Fontoura Costa, Luciano, 2012. "Three-feature model to reproduce the topology of citation networks and the effects from authors’ visibility on their h-index," Journal of Informetrics, Elsevier, vol. 6(3), pages 427-434.
    • Handle: RePEc:eee:infome:v:6:y:2012:i:3:p:427-434
    DOI: 10.1016/j.joi.2012.02.005
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    References listed on IDEAS

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    1. D. R. Amancio & M. G. V. Nunes & O. N. Oliveira & L. F. Costa, 2012. "Using complex networks concepts to assess approaches for citations in scientific papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 827-842, June.
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    5. Adilson Vital & Diego R. Amancio, 2022. "A comparative analysis of local similarity metrics and machine learning approaches: application to link prediction in author citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 6011-6028, October.
    6. Corrêa Jr., Edilson A. & Silva, Filipi N. & da F. Costa, Luciano & Amancio, Diego R., 2017. "Patterns of authors contribution in scientific manuscripts," Journal of Informetrics, Elsevier, vol. 11(2), pages 498-510.
    7. Brito, Ana C.M. & Silva, Filipi N. & Amancio, Diego R., 2021. "Associations between author-level metrics in subsequent time periods," Journal of Informetrics, Elsevier, vol. 15(4).
    8. Brito, Ana C.M. & Silva, Filipi N. & de Arruda, Henrique F. & Comin, Cesar H. & Amancio, Diego R. & Costa, Luciano da F., 2021. "Classification of abrupt changes along viewing profiles of scientific articles," Journal of Informetrics, Elsevier, vol. 15(2).
    9. Viana, Matheus P. & Amancio, Diego R. & da F. Costa, Luciano, 2013. "On time-varying collaboration networks," Journal of Informetrics, Elsevier, vol. 7(2), pages 371-378.
    10. Xiomara S. Q. Chacon & Thiago C. Silva & Diego R. Amancio, 2020. "Comparing the impact of subfields in scientific journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 625-639, October.
    11. Mittal, Shravika & Chakraborty, Tanmoy & Pal, Siddharth, 2022. "Dynamics of node influence in network growth models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    12. Mayra Z Rodriguez & Cesar H Comin & Dalcimar Casanova & Odemir M Bruno & Diego R Amancio & Luciano da F Costa & Francisco A Rodrigues, 2019. "Clustering algorithms: A comparative approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-34, January.
    13. Zhao, Qihang & Feng, Xiaodong, 2022. "Utilizing citation network structure to predict paper citation counts: A Deep learning approach," Journal of Informetrics, Elsevier, vol. 16(1).
    14. KM. Pooja & Samrat Mondal & Joydeep Chandra, 2021. "Exploiting similarities across multiple dimensions for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7525-7560, September.
    15. Kumar, Dhananjay & Bhowmick, Plaban Kumar & Paik, Jiaul H, 2023. "Researcher influence prediction (ResIP) using academic genealogy network," Journal of Informetrics, Elsevier, vol. 17(2).
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