Network of Scientific Concepts: Empirical Analysis and Modeling

Vasyl Palychkov, Mariana Krasnytska, Olesya Mryglod, Yurij Holovatch

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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.
Original languageEnglish
Article number2140001
JournalAdvances in Complex Systems
Volume24
Issue number3-4
Early online date17 Sept 2021
DOIs
Publication statusPublished - 2021

Bibliographical note

Funding Information:
This work was supported in part by the National Academy of Sciences of Ukraine, project KPKBK 6541030 (O.M. and Y.H.) and by the National Research Foundation of Ukraine, project 2020.01/0338 (M.K.).

Publisher Copyright:
© 2021 World Scientific Publishing Company.

Keywords

  • Logology
  • complex systems
  • concepts network
  • generative models
  • semantic networks

ASJC Scopus subject areas

  • Control and Systems Engineering

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