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.
|Journal||Advances in Complex Systems|
|Early online date||17 Sept 2021|
|Publication status||Published - 2021|
Bibliographical noteFunding 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.).
© 2021 World Scientific Publishing Company.
- complex systems
- concepts network
- generative models
- semantic networks
ASJC Scopus subject areas
- Control and Systems Engineering