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A topological analysis of scientific coauthorship networks

Author

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  • Cardillo, Alessio
  • Scellato, Salvatore
  • Latora, Vito

Abstract

We study coauthorship networks based on the preprints submitted to the Los Alamos cond-mat database during the period 2000–2005. In our approach two scientists are considered connected if they have coauthored one or more cond-mat preprints together in the same year. We focus on the characterization of the structural properties of the derived graphs and on the time evolution of such properties. The results show that the cond-mat community has grown over the last six years. This is witnessed by an improvement in the connectivity properties of coauthorship graphs over the years, as confirmed by an increasing size of the largest connected component, of the global efficiency and of the clustering coefficient. We have also found that the graphs are characterized by long-tailed degree and betweenness distributions, assortative degree–degree correlations, and a power-law dependence of the clustering coefficient on the node degree.

Suggested Citation

  • Cardillo, Alessio & Scellato, Salvatore & Latora, Vito, 2006. "A topological analysis of scientific coauthorship networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 372(2), pages 333-339.
    • Handle: RePEc:eee:phsmap:v:372:y:2006:i:2:p:333-339
    DOI: 10.1016/j.physa.2006.08.059
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    Citations

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    Cited by:

    1. Marian-Gabriel Hâncean & Matjaž Perc & Lazăr Vlăsceanu, 2014. "Fragmented Romanian Sociology: Growth and Structure of the Collaboration Network," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-9, November.
    2. Gregorio González-Alcaide & Héctor Pinargote & José M. Ramos, 2020. "From cut-points to key players in co-authorship networks: a case study in ventilator-associated pneumonia research," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 707-733, May.
    3. Li, Jingjing & Zhang, Jian & Li, Huajiao & Jiang, Meihui, 2018. "Network and community structure in a scientific team with high creative performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 702-709.
    4. Leifeld, Philip, 2018. "Polarization in the social sciences: Assortative mixing in social science collaboration networks is resilient to interventions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 510-523.
    5. de Mesnard, Louis, 2017. "Attributing credit to coauthors in academic publishing: The 1/n rule, parallelization, and team bonuses," European Journal of Operational Research, Elsevier, vol. 260(2), pages 778-788.
    6. Takao Furukawa & Nobuyuki Shirakawa & Kumi Okuwada, 2011. "Quantitative analysis of collaborative and mobility networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 451-466, June.
    7. Costa, Luciano da Fontoura & Rodrigues Tognetti, Marilza A. & Silva, Filipi Nascimento, 2008. "Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6201-6214.
    8. Li, Yongjun & You, Chun, 2013. "What is the difference of research collaboration network under different projections: Topological measurement and analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(15), pages 3248-3259.
    9. Noémi Gaskó & Rodica Ioana Lung & Mihai Alexandru Suciu, 2016. "A new network model for the study of scientific collaborations: Romanian computer science and mathematics co-authorship networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 613-632, August.

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