Identifying influential nodes with centrality indices combinations using symbolic regressions
Numerous strategies for determining the most influential nodes in a connected network have been developed. The use of centrality indices in a network allows the identification of the most important nodes in the network. Specific indices, on the other hand, cannot search for a network's entire m...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Science and Information Organization
2022
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| Online Access: | http://eprints.utem.edu.my/id/eprint/26630/2/IDENTIFYING%20INFLUENTIAL%20NODES%20WITH%20CENTRALITY%20INDICES%20COMBINATIONS%20USING%20SYMBOLIC%20REGRESSIONS_COMPRESSED.PDF http://eprints.utem.edu.my/id/eprint/26630/ https://thesai.org/Downloads/Volume13No5/Paper_70-Identifying_Influential_Nodes_with_Centrality_Indices.pdf |
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| Summary: | Numerous strategies for determining the most influential nodes in a connected network have been developed. The use of centrality indices in a network allows the identification of the most important nodes in the network. Specific indices, on the other hand, cannot search for a network's entire meaning because they are only interested in a single attribute. Researchers frequently overlook an index's characteristics in favour of focusing on its application. The purpose of this research is to integrate selected centrality indices classified by their various properties. A symbolic regression approach was used to find meaningful mathematical expressions for this combination of indices. When the efficacy of the combined indices is compared to other methods, the combined indices react similarly and outperform the previous method. Using this adaptive technique, network researchers can now identify the most influential network nodes. |
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