NETWORK OF SCIENTIFIC CONCEPTS: EMPIRICAL ANALYSIS AND MODELING

Vasyl Palchykov (), Mariana Krasnytska, Olesya Mryglod and Yurij Holovatch
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Vasyl Palchykov: 𠕃4 Collaboration and Doctoral College for the Statistical Physics of Complex Systems, Leipzig-Lorraine-Lviv-Coventry, Europe
Mariana Krasnytska: Institute for Condensed Matter Physics, National Academy of Sciences of Ukraine, UA-79011 Lviv, Ukraine1𠕃4 Collaboration and Doctoral College for the Statistical Physics of Complex Systems, Leipzig-Lorraine-Lviv-Coventry, Europe
Olesya Mryglod: Institute for Condensed Matter Physics, National Academy of Sciences of Ukraine, UA-79011 Lviv, Ukraine1𠕃4 Collaboration and Doctoral College for the Statistical Physics of Complex Systems, Leipzig-Lorraine-Lviv-Coventry, Europe
Yurij Holovatch: Institute for Condensed Matter Physics, National Academy of Sciences of Ukraine, UA-79011 Lviv, Ukraine1𠕃4 Collaboration and Doctoral College for the Statistical Physics of Complex Systems, Leipzig-Lorraine-Lviv-Coventry, Europe3Centre for Fluid and Complex Systems, Coventry University, Coventry CV1 5FB, UK

Advances in Complex Systems (ACS), 2021, vol. 24, issue 03n04, 1-23

Abstract: Concepts in a certain domain of science are linked via intrinsic connections reflecting the structure of knowledge. To get a qualitative insight and a quantitative description of this structure, we perform empirical analysis and modeling of the network of scientific concepts in the domain of physics. To this end, we use a collection of manuscripts submitted to the e-print repository arXiv and the vocabulary of scientific concepts collected via the ScienceWISE.info platform and construct a network of scientific concepts based on their co-occurrences in publications. The resulting complex network possesses a number of specific features (high node density, dissortativity, structural correlations, skewed node degree distribution) that cannot be understood as a result of simple growth by several commonly used network models. We show that the model based on a simultaneous account of two factors, growth by blocks and preferential selection, gives an explanation of empirically observed properties of the concepts network.

Keywords: Logology; complex systems; semantic networks; concepts network; generative models (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0219525921400014

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