Network of scientific concepts: empirical analysis and modeling

TL;DR

This paper empirically analyzes and models the network of scientific concepts in physics, revealing complex features that are explained by a novel growth model combining block growth and preferential selection.

Contribution

It introduces a new model that explains the complex structure of scientific concept networks based on empirical data from physics publications.

Findings

The concept network exhibits high node density and dissortativity.

The network's degree distribution is skewed and cannot be explained by simple models.

A combined growth model accounts for the observed network properties.

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 can not 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.