https://doi.org/10.1140/epjb/s10051-023-00608-w
Regular Article - Statistical and Nonlinear Physics
Cross-relation characterization of knowledge networks
1
Mathematical Institute, University of Oxford, Woodstock Rd, OX26GG, Oxford, UK
2
Institute of Physics at São Carlos, University of São Paulo, Av Trabalhador São-carlense, 400, 13566590, São Carlos, Brazil
Received:
8
July
2023
Accepted:
9
October
2023
Published online:
5
November
2023
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.
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© The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
