Cross-relation characterization of knowledge networks
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 neighbor...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Springer Nature
2023
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| _version_ | 1817931339209375744 |
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| author | Tokuda, E Lambiotte, R da F. Costa, L |
| author_facet | Tokuda, E Lambiotte, R da F. Costa, L |
| author_sort | Tokuda, E |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T08:08:19Z |
| format | Journal article |
| id | oxford-uuid:8d33ee72-5499-4d52-9bdc-98e59edc3043 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-12-09T03:20:27Z |
| publishDate | 2023 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:8d33ee72-5499-4d52-9bdc-98e59edc30432024-11-05T14:02:14ZCross-relation characterization of knowledge networksJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:8d33ee72-5499-4d52-9bdc-98e59edc3043EnglishSymplectic ElementsSpringer Nature2023Tokuda, ELambiotte, Rda F. Costa, LKnowledge 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. |
| spellingShingle | Tokuda, E Lambiotte, R da F. Costa, L Cross-relation characterization of knowledge networks |
| title | Cross-relation characterization of knowledge networks |
| title_full | Cross-relation characterization of knowledge networks |
| title_fullStr | Cross-relation characterization of knowledge networks |
| title_full_unstemmed | Cross-relation characterization of knowledge networks |
| title_short | Cross-relation characterization of knowledge networks |
| title_sort | cross relation characterization of knowledge networks |
| work_keys_str_mv | AT tokudae crossrelationcharacterizationofknowledgenetworks AT lambiotter crossrelationcharacterizationofknowledgenetworks AT dafcostal crossrelationcharacterizationofknowledgenetworks |