Cross-relation characterization of knowledge networks

Eric K. Tokuda (), Renaud Lambiotte and Luciano da F. Costa
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Eric K. Tokuda: University of Oxford
Renaud Lambiotte: University of Oxford
Luciano da F. Costa: University of São Paulo

The European Physical Journal B: Condensed Matter and Complex Systems, 2023, vol. 96, issue 11, 1-19

Abstract: Abstract Knowledge networks are large, interconnected data sets of knowledge that can be represented, studied and modeled using complex networks concepts and methodologies. One aspect of particular interest in this type of networks concerns how much the topological properties change along successive neighborhoods of each of the nodes. Another issue of special importance consists in quantifying how much the structure of a knowledge network changes at two different points along time. Here, we report a cross-relation study of two model—theoretical networks (Erdős–Rényi, ER, and Barabási–Albert model, BA) as well as real-world knowledge networks corresponding to the areas of Physics and Theology, obtained from the Wikipedia and taken at two different dates separated by 4 years. The respective two versions of these networks were characterized in terms of their respective cross-relation signatures, being summarized in terms of modification indices obtained for each of the nodes that are preserved among the two versions. It has been observed that the nodes at the core and periphery of both types of theoretical models yielded similar modification indices within these two groups of nodes, but with distinct values when taken across these two groups. The study of the real-world networks indicated that these two networks have signatures, respectively, similar to those of the BA and ER models, as well as that higher modification values tended to occur at the periphery nodes, as compared to the respective core nodes. Graphical abstract

Date: 2023
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DOI: 10.1140/epjb/s10051-023-00608-w

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