Identifying influential nodes with centrality indices combinations using symbolic regressions

Mukhtar, Mohd Fariduddin and Abal Abas, Zuraida and Abdul Rasib, Amir Hamzah and Hairol Anuar, Siti Haryanti and Mohd Zaki, Nurul Hafizah and Abdul Rahman, Ahmad Fadzli Nizam and Zainal Abidin, Zaheera and Shibghatullah, Abdul Samad (2022) Identifying influential nodes with centrality indices combinations using symbolic regressions. International Journal of Advanced Computer Science and Applications, 13 (5). pp. 592-599. ISSN 2158-107X

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Centrality indices, combination, symbolic regressions, influential nodes
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	13 Apr 2023 15:27
Last Modified:	13 Apr 2023 15:27
URI:	http://eprints.utem.edu.my/id/eprint/26630
Statistic Details:	View Download Statistic

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