Global structure model modification to improve influential node detection

Mukhtar, Mohd Fariduddin and Abal Abas, Zuraida and Abdul Rasib, Amir Hamzah and Asmai, Siti Azirah and Hairol Anuar, Siti Haryanti and Mohd Zaki, Nurul Hafizah and Zainal Abidin, Zaheera and Abdul Rahman, Ahmad Fadzli Nizam (2023) Global structure model modification to improve influential node detection. ARPN Journal Of Engineering And Applied Sciences, 18 (3). pp. 220-225. ISSN 1819-6608

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Abstract

Improving a network's robustness and information acceleration requires assessing the value of its nodes, which has been a central issue in network research. The concept of centrality is crucial since it allows for determining the most important nodes. It is possible to find prominent nodes with the help of centrality indices, but they have computational complexity and are limited by the singularity function. The global structure model (GSM) is one method that helps find these impactful nodes. One of the problems with using GSM is that it ignores these nodes' local information. To address this issue, we propose that considering the features of each index individually and then combining them can result in more accurate detection of influential nodes. An experiment incorporated four attributes: global and local impacts, random walk structure, and node position. In this research, we simulate a real-world network using the SIRIR model to derive its propagation process and then verify its efficacy with measures like the Jaccard similarity score and Kendall's correlation coefficient. According to the findings of the experiments, the Degree of Centrality of the local features has a substantial effect when combined with GSM.

Item Type:	Article
Uncontrolled Keywords:	Centrality indices, Combine, SIR.
Divisions:	Faculty Of Mechanical Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	25 Jul 2024 10:46
Last Modified:	25 Jul 2024 10:46
URI:	http://eprints.utem.edu.my/id/eprint/27398
Statistic Details:	View Download Statistic

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