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Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo

Looking for the missing link between graphical models and social network analysis

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A Comment to this article was published on 12 April 2022

The Original Article was published on 27 May 2021

Abstract

In the present contribution we provide a discussion of the paper on “Bayesian graphical models for modern biological applications”. The authors present an extensive review of Bayesian graphical models, which are used for a variety of inferential tasks applied to biology and medicine settings. Our contribution proposes a conceptual connection between two scientific frameworks, graphical models and social network analysis, by highlighting also the role played by network models and random graphs. A bibliometric analysis is performed by exploiting publications collected from online bibliographic archives to map the main themes characterizing the two research fields. Specifically, a co-word network analysis is carried out using visualization tools and thematic evolution maps.

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Notes

  1. The query used includes the words (TS = [“network model*”] NOT TS = [neural]) OR TS = (“graph* model*”) OR TS = (“social network* analysis”). We investigate this scientific production of the two theoretical frameworks in a wider sense, avoiding to overlap neural networks field.

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Authors and Affiliations

  1. Department of Political and Social Studies, University of Salerno, Fisciano, Italy

    Maria Prosperina Vitale & Giuseppe Giordano

  2. Department of Political Sciences, University of Naples Federico II, Naples, Italy

    Giancarlo Ragozini

Authors
  1. Maria Prosperina Vitale
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  2. Giuseppe Giordano
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  3. Giancarlo Ragozini
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Correspondence to Maria Prosperina Vitale.

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Vitale, M.P., Giordano, G. & Ragozini, G. Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo. Stat Methods Appl 31, 269–278 (2022). https://doi.org/10.1007/s10260-021-00603-4

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