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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: Mukhtar, Mohd Fariduddin, Abal Abas, Zuraida, Abdul Rasib, Amir Hamzah, Hairol Anuar, Siti Haryanti, Mohd Zaki, Nurul Hafizah, Abdul Rahman, Ahmad Fadzli Nizam, Zainal Abidin, Zaheera, Shibghatullah, Abdul Samad
Format: Article
Language:English
Published: Science and Information Organization 2022
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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spelling my.utem.eprints.266302023-04-13T15:27:24Z http://eprints.utem.edu.my/id/eprint/26630/ Identifying influential nodes with centrality indices combinations using symbolic regressions Mukhtar, Mohd Fariduddin Abal Abas, Zuraida Abdul Rasib, Amir Hamzah Hairol Anuar, Siti Haryanti Mohd Zaki, Nurul Hafizah Abdul Rahman, Ahmad Fadzli Nizam Zainal Abidin, Zaheera Shibghatullah, Abdul Samad 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. Science and Information Organization 2022 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26630/2/IDENTIFYING%20INFLUENTIAL%20NODES%20WITH%20CENTRALITY%20INDICES%20COMBINATIONS%20USING%20SYMBOLIC%20REGRESSIONS_COMPRESSED.PDF 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 https://thesai.org/Downloads/Volume13No5/Paper_70-Identifying_Influential_Nodes_with_Centrality_Indices.pdf 10.14569/IJACSA.2022.0130570
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description 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.
format Article
author Mukhtar, Mohd Fariduddin
Abal Abas, Zuraida
Abdul Rasib, Amir Hamzah
Hairol Anuar, Siti Haryanti
Mohd Zaki, Nurul Hafizah
Abdul Rahman, Ahmad Fadzli Nizam
Zainal Abidin, Zaheera
Shibghatullah, Abdul Samad
spellingShingle Mukhtar, Mohd Fariduddin
Abal Abas, Zuraida
Abdul Rasib, Amir Hamzah
Hairol Anuar, Siti Haryanti
Mohd Zaki, Nurul Hafizah
Abdul Rahman, Ahmad Fadzli Nizam
Zainal Abidin, Zaheera
Shibghatullah, Abdul Samad
Identifying influential nodes with centrality indices combinations using symbolic regressions
author_facet Mukhtar, Mohd Fariduddin
Abal Abas, Zuraida
Abdul Rasib, Amir Hamzah
Hairol Anuar, Siti Haryanti
Mohd Zaki, Nurul Hafizah
Abdul Rahman, Ahmad Fadzli Nizam
Zainal Abidin, Zaheera
Shibghatullah, Abdul Samad
author_sort Mukhtar, Mohd Fariduddin
title Identifying influential nodes with centrality indices combinations using symbolic regressions
title_short Identifying influential nodes with centrality indices combinations using symbolic regressions
title_full Identifying influential nodes with centrality indices combinations using symbolic regressions
title_fullStr Identifying influential nodes with centrality indices combinations using symbolic regressions
title_full_unstemmed Identifying influential nodes with centrality indices combinations using symbolic regressions
title_sort identifying influential nodes with centrality indices combinations using symbolic regressions
publisher Science and Information Organization
publishDate 2022
url 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
_version_ 1763300014165065728
score 13.211869

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